{"id":87753,"date":"2026-08-02T09:39:45","date_gmt":"2026-08-02T06:39:45","guid":{"rendered":"https:\/\/twodots.gr\/?p=87753"},"modified":"2026-08-02T09:39:47","modified_gmt":"2026-08-02T06:39:47","slug":"rl-sft-reasoning-models-esoterika-representations","status":"publish","type":"post","link":"https:\/\/twodots.gr\/en\/rl-sft-reasoning-models-esoterika-representations\/","title":{"rendered":"RL \u03ae SFT \u03c3\u03c4\u03b1 reasoning models: \u03c4\u03b9 \u03b1\u03c0\u03bf\u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03bf\u03c5\u03bd \u03c4\u03b1 \u03b5\u03c3\u03c9\u03c4\u03b5\u03c1\u03b9\u03ba\u03ac representations"},"content":{"rendered":"<div class=\"td-article-lede\">\n<p><strong>\u03a3\u03cd\u03bd\u03c4\u03bf\u03bc\u03b7 \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7:<\/strong> \u03b7 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03b2\u03c1\u03af\u03c3\u03ba\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03b1 RL-oriented reasoning models \u03c4\u03b5\u03af\u03bd\u03bf\u03c5\u03bd \u03bd\u03b1 \u03c3\u03c7\u03b7\u03bc\u03b1\u03c4\u03af\u03b6\u03bf\u03c5\u03bd \u03c0\u03b9\u03bf \u03ba\u03b1\u03b8\u03b1\u03c1\u03ac, \u03bd\u03c9\u03c1\u03af\u03c4\u03b5\u03c1\u03b1 \u03b1\u03bd\u03b9\u03c7\u03bd\u03b5\u03cd\u03c3\u03b9\u03bc\u03b1 representations \u03c4\u03b7\u03c2 \u03bf\u03c1\u03b8\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03c3\u03c5\u03b3\u03ba\u03b5\u03bd\u03c4\u03c1\u03ce\u03bd\u03bf\u03c5\u03bd \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03b7 \u03b5\u03c0\u03b5\u03be\u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03c3\u03b5 \u03b2\u03b1\u03b8\u03cd\u03c4\u03b5\u03c1\u03b1 layers. \u0394\u03b5\u03bd \u03b2\u03c1\u03af\u03c3\u03ba\u03b5\u03b9, \u03cc\u03bc\u03c9\u03c2, \u03cc\u03c4\u03b9 \u03c4\u03bf RL \u03b5\u03b3\u03b3\u03c5\u03ac\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc \u03bc\u03ae\u03ba\u03bf\u03c2 \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7\u03c2 \u03ae \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c8\u03b9\u03bc\u03bf compute.<\/p>\n<p>\u0391\u03c5\u03c4\u03cc \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03cc \u03bc\u03ae\u03bd\u03c5\u03bc\u03b1 \u03b3\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7: \u03bc\u03b7\u03bd \u03b1\u03b3\u03bf\u03c1\u03ac\u03b6\u03b5\u03c4\u03b5 \u03c4\u03b7\u03bd \u03b5\u03c4\u03b9\u03ba\u03ad\u03c4\u03b1 \u00abreasoning\u00bb \u03ae \u00abRL\u00bb. \u0391\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03ae\u03c3\u03c4\u03b5 \u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf model version \u03c3\u03c4\u03bf \u03b4\u03b9\u03ba\u03cc \u03c3\u03b1\u03c2 workload, \u03bc\u03b5 \u03af\u03b4\u03b9\u03bf prompt \u03ba\u03b1\u03b9 sampling, \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03b1\u03bc\u03b2\u03b1\u03bd\u03cc\u03bc\u03b5\u03bd\u03b1 runs \u03ba\u03b1\u03b9 \u03ba\u03bf\u03b9\u03bd\u03ad\u03c2 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 \u03b3\u03b9\u03b1 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1, latency, tokens \u03ba\u03b1\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2.<\/p>\n<\/div>\n<div class=\"td-article-note\">\n<p><strong>\u03a4\u03bf \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03bf \u03b5\u03cd\u03c1\u03b7\u03bc\u03b1:<\/strong> \u03c4\u03b1 internal representations \u03ba\u03b1\u03b9 \u03b7 \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03bc\u03ae \u03c4\u03b7\u03c2 \u03b4\u03bf\u03c5\u03bb\u03b5\u03b9\u03ac\u03c2 \u03c3\u03c4\u03b1 layers \u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03bf\u03c5\u03bd \u03bc\u03b5\u03c4\u03b1\u03be\u03cd RL-oriented \u03ba\u03b1\u03b9 SFT\/instruction-tuned \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03c9\u03bd, \u03b1\u03bb\u03bb\u03ac \u03b7 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03c9\u03bd output tokens \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03b9\u03b4\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03bf\u03c5 \u03c4\u03bf\u03c5 training \u03ba\u03b1\u03b9 deployment pipeline.<\/p>\n<\/div>\n<div class=\"td-article-toc\">\n<div class=\"td-toc-title\">Contents<\/div>\n<ul>\n<li><a href=\"#ti-sygkrinei-meleti\">\u03a4\u03b9 \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03b7 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7<\/a><\/li>\n<li><a href=\"#probing-hidden-states\">\u03a0\u03ce\u03c2 \u03c3\u03c4\u03ae\u03b8\u03b7\u03ba\u03b5 \u03c4\u03bf probing \u03c4\u03c9\u03bd hidden states<\/a><\/li>\n<li><a href=\"#linear-probes-evrimata\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b1\u03bd \u03c4\u03b1 linear probes<\/a><\/li>\n<li><a href=\"#mean-ablation-layers\">Mean ablation: \u03c0\u03bf\u03b9\u03b1 layers \u03b1\u03bb\u03bb\u03ac\u03b6\u03bf\u03c5\u03bd \u03c4\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1<\/a><\/li>\n<li><a href=\"#token-variability-compute\">Token variability: \u03b3\u03b9\u03b1\u03c4\u03af \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b1 tokens \u03b4\u03b5\u03bd \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03bf\u03c5\u03bd \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03bc\u03ae<\/a><\/li>\n<li><a href=\"#arithmitiko-keno-paper\">\u03a4\u03bf \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03cc \u03ba\u03b5\u03bd\u03cc \u03c0\u03bf\u03c5 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c0\u03c1\u03bf\u03c3\u03bf\u03c7\u03ae<\/a><\/li>\n<li><a href=\"#ai-procurement-simasia\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03bf\u03c5\u03bd \u03c4\u03b1 \u03b5\u03c5\u03c1\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b3\u03b9\u03b1 AI procurement<\/a><\/li>\n<li><a href=\"#axiologisi-reasoning-model-praktika\">\u03a0\u03ce\u03c2 \u03bd\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03ae\u03c3\u03b5\u03c4\u03b5 \u03ad\u03bd\u03b1 reasoning model \u03c3\u03c4\u03b7\u03bd \u03c0\u03c1\u03ac\u03be\u03b7<\/a><\/li>\n<li><a href=\"#oria-ergasias\">\u03a4\u03b9 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03b7 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1<\/a><\/li>\n<li><a href=\"#asfales-epicheirimatiko-symperasma\">\u03a4\u03bf \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03c3\u03c5\u03bc\u03c0\u03ad\u03c1\u03b1\u03c3\u03bc\u03b1<\/a><\/li>\n<\/ul>\n<\/div>\n<h2 id=\"ti-sygkrinei-meleti\">\u03a4\u03b9 \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03b7 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7<\/h2>\n<p>\u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 <em>Probing the Origins of Reasoning Performance<\/em> \u03b4\u03b5\u03bd \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03ad\u03bd\u03b1 \u03b1\u03ba\u03cc\u03bc\u03b7 benchmark score. \u03a1\u03c9\u03c4\u03ac \u03b1\u03bd \u03b7 \u03bc\u03ad\u03b8\u03bf\u03b4\u03bf\u03c2 \u03b5\u03ba\u03c0\u03b1\u03af\u03b4\u03b5\u03c5\u03c3\u03b7\u03c2 \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03c4\u03bf\u03bd \u03c4\u03c1\u03cc\u03c0\u03bf \u03bc\u03b5 \u03c4\u03bf\u03bd \u03bf\u03c0\u03bf\u03af\u03bf \u03ad\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bf\u03c1\u03b3\u03b1\u03bd\u03ce\u03bd\u03b5\u03b9 \u03b5\u03c3\u03c9\u03c4\u03b5\u03c1\u03b9\u03ba\u03ac \u03c4\u03b7\u03bd \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03af\u03b1, \u03ba\u03b1\u03c4\u03b1\u03bd\u03ad\u03bc\u03b5\u03b9 \u03c4\u03bf\u03bd \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03bc\u03cc \u03c3\u03c4\u03b1 transformer layers \u03ba\u03b1\u03b9 \u03b1\u03c0\u03bf\u03c6\u03b1\u03c3\u03af\u03b6\u03b5\u03b9 \u03c0\u03cc\u03c3\u03bf \u03bc\u03b5\u03b3\u03ac\u03bb\u03b7 \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7 \u03b8\u03b1 \u03c0\u03b1\u03c1\u03ac\u03b3\u03b5\u03b9.<\/p>\n<p>\u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03bf\u03c5\u03bd \u03c0\u03b1\u03c1\u03b1\u03bb\u03bb\u03b1\u03b3\u03ad\u03c2 \u03c4\u03c9\u03bd \u03bf\u03b9\u03ba\u03bf\u03b3\u03b5\u03bd\u03b5\u03b9\u03ce\u03bd DeepSeek-Math \u03ba\u03b1\u03b9 Olmo 3 \u03bc\u03b5 \u03c4\u03c1\u03b5\u03b9\u03c2 \u03c3\u03c5\u03bc\u03c0\u03bb\u03b7\u03c1\u03c9\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c0\u03c1\u03bf\u03c3\u03b5\u03b3\u03b3\u03af\u03c3\u03b5\u03b9\u03c2: linear probes \u03c3\u03c4\u03b1 layer-wise hidden states, mean ablations \u03b1\u03bd\u03ac layer \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03b1\u03bc\u03b2\u03b1\u03bd\u03cc\u03bc\u03b5\u03bd\u03b5\u03c2 \u03b4\u03b5\u03b9\u03b3\u03bc\u03b1\u03c4\u03bf\u03bb\u03b7\u03c8\u03af\u03b5\u03c2 \u03b3\u03b9\u03b1 \u03c4\u03b7 \u03bc\u03b5\u03c4\u03b1\u03b2\u03bb\u03b7\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03c9\u03bd output tokens. \u03a4\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03ad\u03bd\u03b1 \u03b1\u03c0\u03bb\u03bf\u03ca\u03ba\u03cc \u00abRL \u03ba\u03b1\u03bb\u03cc, SFT \u03ba\u03b1\u03ba\u03cc\u00bb. \u03a4\u03b1 RL-oriented \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03bf\u03c5\u03bd \u03ba\u03b1\u03b8\u03b1\u03c1\u03cc\u03c4\u03b5\u03c1\u03b1 correctness signals, \u03b5\u03bd\u03ce \u03b7 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03bf\u03c5 \u03bc\u03ae\u03ba\u03bf\u03c5\u03c2 \u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03ad\u03bd\u03c4\u03bf\u03bd\u03b1 \u03b1\u03bd\u03ac model family.<\/p>\n<p>\u0391\u03c5\u03c4\u03ae \u03b7 \u03b4\u03b9\u03ac\u03ba\u03c1\u03b9\u03c3\u03b7 \u03c3\u03c5\u03bc\u03c0\u03bb\u03b7\u03c1\u03ce\u03bd\u03b5\u03b9 \u03cc\u03c3\u03b1 \u03b2\u03bb\u03ad\u03c0\u03bf\u03c5\u03bc\u03b5 \u03cc\u03c4\u03b1\u03bd \u03ad\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf <a href=\"https:\/\/twodots.gr\/overthinking-ai-montela-enischymeni-skepsi-krymmeni-gnosi\/\">\u03c0\u03b1\u03c1\u03ac\u03b3\u03b5\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf reasoning \u03c7\u03c9\u03c1\u03af\u03c2 \u03b1\u03bd\u03ac\u03bb\u03bf\u03b3\u03b7 \u03b2\u03b5\u03bb\u03c4\u03af\u03c9\u03c3\u03b7<\/a>. \u0397 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b1\u03bb\u03c5\u03c3\u03af\u03b4\u03b1 \u03c3\u03ba\u03ad\u03c8\u03b7\u03c2 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03cc \u03bc\u03cc\u03bd\u03b7 \u03c4\u03b7\u03c2 \u03b1\u03c0\u03cc\u03b4\u03b5\u03b9\u03be\u03b7 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7\u03c2 \u03b5\u03c3\u03c9\u03c4\u03b5\u03c1\u03b9\u03ba\u03ae\u03c2 \u03bf\u03c1\u03b3\u03ac\u03bd\u03c9\u03c3\u03b7\u03c2 \u03ae \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae\u03c2 \u03b1\u03c0\u03cc\u03b4\u03bf\u03c3\u03b7\u03c2.<\/p>\n<div class=\"td-comparison\">\n<p class=\"td-comparison-title\">\u03a4\u03ad\u03c3\u03c3\u03b5\u03c1\u03b9\u03c2 \u03b4\u03b9\u03b1\u03c3\u03c4\u03ac\u03c3\u03b5\u03b9\u03c2 \u03c4\u03b7\u03c2 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7\u03c2 RL \u03ba\u03b1\u03b9 SFT<\/p>\n<div class=\"td-comparison-cards td-comparison-cards--horizontal\">\n<div class=\"td-comparison-grid td-comparison-grid--two\">\n<div class=\"td-platform-card\">\n<h3>Representation signal<\/h3>\n<p>\u03a4\u03b1 RL-oriented \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03ad\u03c6\u03c4\u03b1\u03c3\u03b1\u03bd 83%\u201398% probe accuracy, \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 75%\u201390% \u03b3\u03b9\u03b1 \u03c4\u03b1 instruction-tuned \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">Linear probe<\/span><span class=\"td-badge\">\u039f\u03c1\u03b8\u03cc\u03c4\u03b7\u03c4\u03b1<\/span><\/div>\n<\/div>\n<div class=\"td-platform-card td-platform-card--navy\">\n<h3>Layer criticality<\/h3>\n<p>\u03a3\u03c4\u03bf DeepSeek-Math-RL \u03b7 \u03b5\u03c0\u03af\u03b4\u03c1\u03b1\u03c3\u03b7 \u03c4\u03b7\u03c2 ablation \u03b1\u03c5\u03be\u03b1\u03bd\u03cc\u03c4\u03b1\u03bd \u03bc\u03b5 \u03c4\u03bf \u03b2\u03ac\u03b8\u03bf\u03c2, \u03b5\u03bd\u03ce \u03c3\u03c4\u03bf Instruct \u03ad\u03bc\u03b5\u03b9\u03bd\u03b5 \u03c3\u03c7\u03b5\u03b4\u03cc\u03bd \u03b5\u03c0\u03af\u03c0\u03b5\u03b4\u03b7.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">r=0,47<\/span><span class=\"td-badge\">Mean ablation<\/span><\/div>\n<\/div>\n<div class=\"td-platform-card\">\n<h3>Token variability<\/h3>\n<p>\u03a4\u03bf DeepSeek-Math-RL \u03b5\u03af\u03c7\u03b5 \u03c5\u03c8\u03b7\u03bb\u03ae \u03bc\u03b5\u03c4\u03b1\u03b2\u03bb\u03b7\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1 \u03bc\u03ae\u03ba\u03bf\u03c5\u03c2, \u03b1\u03bb\u03bb\u03ac \u03c4\u03b1 \u03b4\u03cd\u03bf Olmo 3 variants \u03c0\u03b1\u03c1\u03ad\u03bc\u03b5\u03b9\u03bd\u03b1\u03bd \u03c0\u03b1\u03c1\u03cc\u03bc\u03bf\u03b9\u03b1 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ac.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">CV<\/span><span class=\"td-badge\">Family effect<\/span><\/div>\n<\/div>\n<div class=\"td-platform-card td-platform-card--navy\">\n<h3>Business decision<\/h3>\n<p>\u0397 \u03b5\u03c4\u03b9\u03ba\u03ad\u03c4\u03b1 RL \u03ae SFT \u03b4\u03b5\u03bd \u03b1\u03c1\u03ba\u03b5\u03af\u00b7 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03b5\u03af\u03c4\u03b1\u03b9 \u03c4\u03bf \u03b1\u03ba\u03c1\u03b9\u03b2\u03ad\u03c2 model version \u03bc\u03b1\u03b6\u03af \u03bc\u03b5 prompt, sampling \u03ba\u03b1\u03b9 deployment stack.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">Workload<\/span><span class=\"td-badge\">Repeatability<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<h2 id=\"probing-hidden-states\">\u03a0\u03ce\u03c2 \u03c3\u03c4\u03ae\u03b8\u03b7\u03ba\u03b5 \u03c4\u03bf probing \u03c4\u03c9\u03bd hidden states<\/h2>\n<p>\u0393\u03b9\u03b1 \u03c4\u03bf probing \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03ae\u03b8\u03b7\u03ba\u03b1\u03bd 1.000 \u03c3\u03c5\u03bd\u03b8\u03b5\u03c4\u03b9\u03ba\u03ac \u03bc\u03b1\u03b8\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03c0\u03c1\u03bf\u03b2\u03bb\u03ae\u03bc\u03b1\u03c4\u03b1 \u03bc\u03b5 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03b5\u03c2 \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03bf\u03c5\u03c2. \u0397 \u03c3\u03c5\u03bd\u03b8\u03b5\u03c4\u03b9\u03ba\u03ae \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c4\u03bf\u03bd \u03ba\u03af\u03bd\u03b4\u03c5\u03bd\u03bf contamination, \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9 \u03b1\u03bb\u03b3\u03bf\u03c1\u03b9\u03b8\u03bc\u03b9\u03ba\u03cc \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf \u03c4\u03bf\u03c5 ground truth \u03ba\u03b1\u03b9 \u03b4\u03af\u03bd\u03b5\u03b9 \u03b1\u03c1\u03ba\u03b5\u03c4\u03ac \u03b4\u03b5\u03af\u03b3\u03bc\u03b1\u03c4\u03b1 \u03b3\u03b9\u03b1 \u03c3\u03c4\u03b1\u03c4\u03b9\u03c3\u03c4\u03b9\u03ba\u03ae \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7. \u0394\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03b3\u03b5\u03bd\u03af\u03ba\u03b5\u03c5\u03c3\u03b7 \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 \u03b5\u03af\u03b4\u03bf\u03c2 reasoning, \u03b1\u03bb\u03bb\u03ac \u03b1\u03c0\u03bf\u03bc\u03bf\u03bd\u03ce\u03bd\u03b5\u03b9 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b1 \u03c4\u03bf \u03c6\u03b1\u03b9\u03bd\u03cc\u03bc\u03b5\u03bd\u03bf \u03c0\u03bf\u03c5 \u03b5\u03be\u03b5\u03c4\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9.<\/p>\n<p>\u039a\u03ac\u03b8\u03b5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c0\u03b1\u03c1\u03ae\u03b3\u03b1\u03b3\u03b5 \u03bc\u03af\u03b1 \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7 \u03b1\u03bd\u03ac \u03c0\u03c1\u03cc\u03b2\u03bb\u03b7\u03bc\u03b1 \u03bc\u03b5 temperature 0,6\u20130,7 \u03ba\u03b1\u03b9 top-p 0,95. \u0397 \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae \u03c4\u03b9\u03bc\u03ae \u03b5\u03be\u03b1\u03b3\u03cc\u03c4\u03b1\u03bd \u03b1\u03c0\u03cc \u03c4\u03bf <code>boxed<\/code> delimiter \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b9\u03c4\u03c1\u03b5\u03c0\u03cc\u03c4\u03b1\u03bd \u03b1\u03bd\u03bf\u03c7\u03ae \u00b11 \u03b3\u03b9\u03b1 \u03c3\u03c4\u03c1\u03bf\u03b3\u03b3\u03c5\u03bb\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7. \u039f\u03b9 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03ad\u03c2 \u03ba\u03c1\u03ac\u03c4\u03b7\u03c3\u03b1\u03bd \u03c4\u03bf \u03ba\u03bf\u03b9\u03bd\u03cc \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf \u03c0\u03c1\u03bf\u03b2\u03bb\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd \u03c0\u03bf\u03c5 \u03b1\u03c0\u03b1\u03bd\u03c4\u03ae\u03b8\u03b7\u03ba\u03b5 \u03b1\u03c0\u03cc \u03cc\u03bb\u03b1 \u03c4\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1, \u03b5\u03be\u03b9\u03c3\u03bf\u03c1\u03c1\u03cc\u03c0\u03b7\u03c3\u03b1\u03bd \u03c3\u03c9\u03c3\u03c4\u03ac \u03ba\u03b1\u03b9 \u03bb\u03b1\u03bd\u03b8\u03b1\u03c3\u03bc\u03ad\u03bd\u03b1 \u03b4\u03b5\u03af\u03b3\u03bc\u03b1\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b1\u03bd \u03b4\u03b9\u03b1\u03c7\u03c9\u03c1\u03b9\u03c3\u03bc\u03cc 70% training, 15% validation \u03ba\u03b1\u03b9 15% test.<\/p>\n<p>\u03a4\u03b1 activations \u03c3\u03c5\u03bb\u03bb\u03ad\u03c7\u03b8\u03b7\u03ba\u03b1\u03bd \u03b1\u03ba\u03c1\u03b9\u03b2\u03ce\u03c2 \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03c4\u03bf delimiter \u03c4\u03b7\u03c2 \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae\u03c2 \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7\u03c2, \u03b4\u03b7\u03bb\u03b1\u03b4\u03ae \u03b1\u03c6\u03bf\u03cd \u03b5\u03af\u03c7\u03b5 \u03c0\u03b1\u03c1\u03b1\u03c7\u03b8\u03b5\u03af \u03c4\u03bf reasoning \u03b1\u03bb\u03bb\u03ac \u03c0\u03c1\u03b9\u03bd \u03b5\u03ba\u03c6\u03c1\u03b1\u03c3\u03c4\u03b5\u03af \u03bf \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc\u03c2. \u0393\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 layer \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03cd\u03c4\u03b7\u03ba\u03b5 logistic regression \u03bc\u03b5 5-fold cross-validation. \u0397 \u03b1\u03c0\u03bb\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03bf\u03c5 probe \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03ae: \u03b1\u03bd \u03ad\u03bd\u03b1\u03c2 \u03b3\u03c1\u03b1\u03bc\u03bc\u03b9\u03ba\u03cc\u03c2 classifier \u03b4\u03b9\u03b1\u03c7\u03c9\u03c1\u03af\u03b6\u03b5\u03b9 \u03b1\u03be\u03b9\u03cc\u03c0\u03b9\u03c3\u03c4\u03b1 \u03c3\u03c9\u03c3\u03c4\u03ad\u03c2 \u03ba\u03b1\u03b9 \u03bb\u03b1\u03bd\u03b8\u03b1\u03c3\u03bc\u03ad\u03bd\u03b5\u03c2 \u03bb\u03cd\u03c3\u03b5\u03b9\u03c2, \u03b7 \u03c3\u03c7\u03b5\u03c4\u03b9\u03ba\u03ae \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03af\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03b9\u03bf \u03ba\u03b1\u03b8\u03b1\u03c1\u03ac linearly separable.<\/p>\n<h2 id=\"linear-probes-evrimata\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b1\u03bd \u03c4\u03b1 linear probes<\/h2>\n<p>\u03a3\u03cd\u03bc\u03c6\u03c9\u03bd\u03b1 \u03bc\u03b5 \u03c4\u03bf Figure 1, \u03c4\u03b1 DeepSeek-Math-7B-RL \u03ba\u03b1\u03b9 Olmo-3-Think \u03c0\u03ad\u03c4\u03c5\u03c7\u03b1\u03bd probe accuracy 83%\u201398%, \u03b5\u03bd\u03ce \u03c4\u03b1 instruction-tuned \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03ba\u03b9\u03bd\u03ae\u03b8\u03b7\u03ba\u03b1\u03bd \u03c3\u03c4\u03bf 75%\u201390%. \u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac \u03bf\u03ba\u03c4\u03ce \u03c0\u03bf\u03c3\u03bf\u03c3\u03c4\u03b9\u03b1\u03af\u03c9\u03bd \u03bc\u03bf\u03bd\u03ac\u03b4\u03c9\u03bd \u03ba\u03b1\u03b9 \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03b7 \u03b4\u03b9\u03b1\u03ba\u03cd\u03bc\u03b1\u03bd\u03c3\u03b7 \u03bc\u03b5\u03c4\u03b1\u03be\u03cd \u03b4\u03b5\u03b9\u03b3\u03bc\u03ac\u03c4\u03c9\u03bd \u03b3\u03b9\u03b1 \u03c4\u03b1 reasoning \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1.<\/p>\n<p>\u0397 \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03af\u03b1 \u03c0\u03b5\u03c1\u03af \u03bf\u03c1\u03b8\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03c3\u03c4\u03b7\u03ba\u03b5 \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03bd\u03c9\u03c1\u03af\u03c4\u03b5\u03c1\u03b1. \u03a3\u03c4\u03bf layer 0 \u03b7 test accuracy \u03ae\u03c4\u03b1\u03bd \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 70% \u03b3\u03b9\u03b1 \u03c4\u03b1 reasoning-capable \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03ba\u03b1\u03b9 65% \u03b3\u03b9\u03b1 \u03c4\u03b1 Instruct. \u03a4\u03b1 Olmo-3-Think \u03ba\u03b1\u03b9 DeepSeek-Math-7B-RL \u03b4\u03b9\u03b1\u03c4\u03ae\u03c1\u03b7\u03c3\u03b1\u03bd \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 94%\u201395% \u03c3\u03c4\u03b1 layers 15\u201329.<\/p>\n<p>\u0397 \u03b5\u03c1\u03bc\u03b7\u03bd\u03b5\u03af\u03b1 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c6\u03c1\u03ad\u03bd\u03bf. \u0388\u03bd\u03b1 linear probe \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03ad\u03bd\u03b1 signal \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03be\u03b1\u03c7\u03b8\u03b5\u03af \u03b1\u03c0\u03cc \u03c4\u03bf hidden state\u00b7 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf layer \u03ae \u03c4\u03bf \u03af\u03b4\u03b9\u03bf \u03c4\u03bf signal \u03c0\u03c1\u03bf\u03ba\u03ac\u03bb\u03b5\u03c3\u03b5 \u03c4\u03b7 \u03c3\u03c9\u03c3\u03c4\u03ae \u03bb\u03cd\u03c3\u03b7. \u0393\u03b9\u03b1 \u03b1\u03c5\u03c4\u03cc \u03b7 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03c0\u03c1\u03bf\u03c3\u03b8\u03ad\u03c4\u03b5\u03b9 \u03c0\u03b1\u03c1\u03b5\u03bc\u03b2\u03ac\u03c3\u03b5\u03b9\u03c2 mean ablation \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03bc\u03ad\u03bd\u03b5\u03b9 \u03c3\u03b5 \u03bc\u03b9\u03b1 \u03c0\u03b5\u03c1\u03b9\u03b3\u03c1\u03b1\u03c6\u03b9\u03ba\u03ae \u03c3\u03c5\u03c3\u03c7\u03ad\u03c4\u03b9\u03c3\u03b7. \u03a0\u03b1\u03c1\u03cc\u03bc\u03bf\u03b9\u03b1, \u03b7 <a href=\"https:\/\/twodots.gr\/spark-techniti-noimosyni-xerei-apantisi-den-energopoiei-sosti-skepsi\/\">\u03b4\u03b9\u03b1\u03b8\u03b5\u03c3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c3\u03c9\u03c3\u03c4\u03ae\u03c2 \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03af\u03b1\u03c2 \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf<\/a> \u03b4\u03b5\u03bd \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b8\u03b1 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b7\u03b8\u03b5\u03af \u03c3\u03c9\u03c3\u03c4\u03ac \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae.<\/p>\n<div class=\"td-chart td-chart--metrics\">\n<p class=\"td-chart-title\">\u039f\u03b9 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c0\u03bf\u03c5 \u03c3\u03c4\u03b7\u03c1\u03af\u03b6\u03bf\u03c5\u03bd \u03c4\u03b1 \u03b2\u03b1\u03c3\u03b9\u03ba\u03ac \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03ac\u03c3\u03bc\u03b1\u03c4\u03b1<\/p>\n<p class=\"td-chart-intro\">\u039f\u03b9 \u03c4\u03b9\u03bc\u03ad\u03c2 \u03b1\u03bd\u03ae\u03ba\u03bf\u03c5\u03bd \u03c3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03cc \u03c0\u03c1\u03c9\u03c4\u03cc\u03ba\u03bf\u03bb\u03bb\u03bf \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03bf\u03cd\u03bd \u03b3\u03b5\u03bd\u03b9\u03ba\u03cc score \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 reasoning model.<\/p>\n<div class=\"td-metric-grid\">\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">1.000<\/span><strong>\u03c3\u03c5\u03bd\u03b8\u03b5\u03c4\u03b9\u03ba\u03ac \u03c0\u03c1\u03bf\u03b2\u03bb\u03ae\u03bc\u03b1\u03c4\u03b1<\/strong><\/p>\n<p>\u03a7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03b8\u03b7\u03ba\u03b1\u03bd \u03b3\u03b9\u03b1 \u03c4\u03bf layer-wise probing \u03bc\u03b5 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03b7 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae \u03ba\u03b1\u03b9 \u03b1\u03bb\u03b3\u03bf\u03c1\u03b9\u03b8\u03bc\u03b9\u03ba\u03cc ground truth.<\/p>\n<\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">83%\u201398%<\/span><strong>probe accuracy<\/strong><\/p>\n<p>\u03a4\u03bf \u03b5\u03cd\u03c1\u03bf\u03c2 \u03c0\u03bf\u03c5 \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03b7 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03b3\u03b9\u03b1 \u03c4\u03b1 RL-oriented reasoning models.<\/p>\n<\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">r=0,47<\/span><strong>\u03c3\u03c5\u03c3\u03c7\u03ad\u03c4\u03b9\u03c3\u03b7 \u03b2\u03ac\u03b8\u03bf\u03c5\u03c2<\/strong><\/p>\n<p>\u0397 \u03b8\u03b5\u03c4\u03b9\u03ba\u03ae \u03c3\u03c7\u03ad\u03c3\u03b7 layer depth \u03ba\u03b1\u03b9 accuracy drop \u03c3\u03c4\u03bf DeepSeek-Math-7B-RL, \u03bc\u03b5 p&lt;0,01.<\/p>\n<\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">CV\u22481,3<\/span><strong>\u03bc\u03ad\u03b3\u03b9\u03c3\u03c4\u03b7 \u03bc\u03b5\u03c4\u03b1\u03b2\u03bb\u03b7\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1<\/strong><\/p>\n<p>\u0397 \u03ba\u03bf\u03c1\u03c5\u03c6\u03ae \u03c4\u03bf\u03c5 token coefficient of variation \u03b3\u03b9\u03b1 \u03c4\u03bf DeepSeek-Math-RL \u03c3\u03c4\u03b7 \u03b6\u03ce\u03bd\u03b7 40%\u201360% accuracy.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<h2 id=\"mean-ablation-layers\">Mean ablation: \u03c0\u03bf\u03b9\u03b1 layers \u03b1\u03bb\u03bb\u03ac\u03b6\u03bf\u03c5\u03bd \u03c4\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1<\/h2>\n<p>\u03a4\u03bf probing \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03bc\u03b9\u03b1 \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03af\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03b7. \u0397 mean ablation \u03b5\u03be\u03b5\u03c4\u03ac\u03b6\u03b5\u03b9 \u03b1\u03bd \u03b7 \u03c3\u03c5\u03bd\u03b5\u03b9\u03c3\u03c6\u03bf\u03c1\u03ac \u03b5\u03bd\u03cc\u03c2 layer \u03b5\u03af\u03bd\u03b1\u03b9 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b9\u03ba\u03ac \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03ae: \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03b8\u03b9\u03c3\u03c4\u03ac \u03c4\u03b1 activations \u03c4\u03bf\u03c5 \u03bc\u03b5 \u03c4\u03b7 \u03bc\u03ad\u03c3\u03b7 \u03b5\u03bd\u03b5\u03c1\u03b3\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03b1\u03c0\u03cc dataset \u03b1\u03bd\u03b1\u03c6\u03bf\u03c1\u03ac\u03c2 \u03ba\u03b1\u03b9 \u03bc\u03b5\u03c4\u03c1\u03ac \u03c4\u03b7\u03bd \u03c0\u03c4\u03ce\u03c3\u03b7 \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1\u03c2. \u0397 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ae \u03ad\u03b3\u03b9\u03bd\u03b5 \u03c3\u03b5 20 \u03c0\u03c1\u03bf\u03b2\u03bb\u03ae\u03bc\u03b1\u03c4\u03b1 GSM8K \u03b3\u03b9\u03b1 \u03c4\u03b1 DeepSeek-Math-7B-RL \u03ba\u03b1\u03b9 DeepSeek-Math-7B-Instruct.<\/p>\n<p>\u0397 \u03b2\u03b1\u03c3\u03b9\u03ba\u03ae \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1 \u03ae\u03c4\u03b1\u03bd 70% \u03b3\u03b9\u03b1 \u03c4\u03bf RL \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03ba\u03b1\u03b9 65% \u03b3\u03b9\u03b1 \u03c4\u03bf Instruct. \u03a3\u03c4\u03bf RL, \u03b7 \u03c3\u03c5\u03c3\u03c7\u03ad\u03c4\u03b9\u03c3\u03b7 \u03bc\u03b5\u03c4\u03b1\u03be\u03cd \u03b2\u03ac\u03b8\u03bf\u03c5\u03c2 layer \u03ba\u03b1\u03b9 \u03b5\u03c0\u03af\u03b4\u03c1\u03b1\u03c3\u03b7\u03c2 \u03c4\u03b7\u03c2 \u03c0\u03b1\u03c1\u03ad\u03bc\u03b2\u03b1\u03c3\u03b7\u03c2 \u03ae\u03c4\u03b1\u03bd \u03b8\u03b5\u03c4\u03b9\u03ba\u03ae, r=0,47 \u03bc\u03b5 p&lt;0,01. \u03a3\u03c4\u03bf Instruct \u03ae\u03c4\u03b1\u03bd r=-0,11 \u03bc\u03b5 p=0,55. \u03a4\u03bf \u03c0\u03c1\u03ce\u03c4\u03bf \u03bc\u03bf\u03c4\u03af\u03b2\u03bf \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03b1 \u03b2\u03b1\u03b8\u03cd\u03c4\u03b5\u03c1\u03b1 layers \u03b3\u03af\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c0\u03c1\u03bf\u03bf\u03b4\u03b5\u03c5\u03c4\u03b9\u03ba\u03ac \u03c0\u03b9\u03bf \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03b1, \u03b5\u03bd\u03ce \u03c4\u03bf \u03b4\u03b5\u03cd\u03c4\u03b5\u03c1\u03bf \u03c4\u03b1\u03b9\u03c1\u03b9\u03ac\u03b6\u03b5\u03b9 \u03c3\u03b5 \u03c0\u03b9\u03bf \u03b5\u03c0\u03af\u03c0\u03b5\u03b4\u03b7 \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03bc\u03ae \u03c3\u03b7\u03bc\u03b1\u03c3\u03af\u03b1\u03c2.<\/p>\n<p>\u0397 \u03c3\u03c5\u03b3\u03ba\u03ad\u03bd\u03c4\u03c1\u03c9\u03c3\u03b7 \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03b7\u03c2 \u03b5\u03c0\u03b5\u03be\u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2 \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03c3\u03c4\u03b7\u03ba\u03b5 \u03ba\u03c5\u03c1\u03af\u03c9\u03c2 \u03c3\u03c4\u03b1 layers 9\u201318 \u03ba\u03b1\u03b9 22\u201326 \u03c4\u03bf\u03c5 RL \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5. \u039c\u03ad\u03c7\u03c1\u03b9 \u03c4\u03bf layer 10 \u03c4\u03b1 \u03b4\u03cd\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03b5\u03af\u03c7\u03b1\u03bd \u03c0\u03b1\u03c1\u03cc\u03bc\u03bf\u03b9\u03b1 \u03b5\u03c5\u03c0\u03ac\u03b8\u03b5\u03b9\u03b1, \u03b1\u03bb\u03bb\u03ac \u03bc\u03b5\u03c4\u03ac \u03c4\u03bf layer 15 \u03bf\u03b9 \u03c4\u03c1\u03bf\u03c7\u03b9\u03ad\u03c2 \u03b1\u03c0\u03bf\u03ba\u03bb\u03af\u03bd\u03bf\u03c5\u03bd. \u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03c4\u03bf \u03b5\u03c1\u03bc\u03b7\u03bd\u03b5\u03cd\u03bf\u03c5\u03bd \u03c9\u03c2 \u03c0\u03b9\u03bf \u03b9\u03b5\u03c1\u03b1\u03c1\u03c7\u03b9\u03ba\u03ae \u03bf\u03c1\u03b3\u03ac\u03bd\u03c9\u03c3\u03b7 \u03c3\u03c4\u03bf RL \u03ba\u03b1\u03b9 \u03c0\u03b9\u03bf \u03ba\u03b1\u03c4\u03b1\u03bd\u03b5\u03bc\u03b7\u03bc\u03ad\u03bd\u03b7, \u03c0\u03bb\u03b5\u03bf\u03bd\u03ac\u03b6\u03bf\u03c5\u03c3\u03b1 \u03b5\u03c0\u03b5\u03be\u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03c3\u03c4\u03bf instruction-tuned \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf.<\/p>\n<h2 id=\"token-variability-compute\">Token variability: \u03b3\u03b9\u03b1\u03c4\u03af \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b1 tokens \u03b4\u03b5\u03bd \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03bf\u03c5\u03bd \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03bc\u03ae<\/h2>\n<p>\u0397 \u03c4\u03c1\u03af\u03c4\u03b7 \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af 50 \u03c0\u03c1\u03bf\u03b2\u03bb\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b1\u03c0\u03cc \u03c4\u03bf GSM8K-Platinum \u03ba\u03b1\u03b9 50 \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03b5\u03c2 \u03b1\u03c0\u03b1\u03bd\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2 \u03b1\u03bd\u03ac \u03c0\u03c1\u03cc\u03b2\u03bb\u03b7\u03bc\u03b1 \u03ba\u03b1\u03b9 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf. \u039c\u03b5\u03c4\u03c1\u03ac answer consistency, \u03b4\u03b9\u03ac\u03bc\u03b5\u03c3\u03bf output tokens \u03ba\u03b1\u03b9 coefficient of variation, \u03b4\u03b7\u03bb\u03b1\u03b4\u03ae \u03c4\u03b7\u03bd \u03c4\u03c5\u03c0\u03b9\u03ba\u03ae \u03b1\u03c0\u03cc\u03ba\u03bb\u03b9\u03c3\u03b7 \u03b4\u03b9\u03b1\u03b9\u03c1\u03b5\u03bc\u03ad\u03bd\u03b7 \u03bc\u03b5 \u03c4\u03bf\u03bd \u03bc\u03ad\u03c3\u03bf \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc tokens.<\/p>\n<p>\u039f CV \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03c9\u03bd \u03bc\u03b5 \u03c0\u03bf\u03bb\u03cd \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc \u03bc\u03ae\u03ba\u03bf\u03c2 \u03b5\u03be\u03cc\u03b4\u03bf\u03c5, \u03ba\u03b1\u03b8\u03ce\u03c2 \u03c4\u03b1 reasoning models \u03c3\u03c4\u03b7\u03bd \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03c0\u03b1\u03c1\u03ae\u03b3\u03b1\u03b3\u03b1\u03bd \u03c3\u03c5\u03bd\u03ae\u03b8\u03c9\u03c2 5\u201310 \u03c6\u03bf\u03c1\u03ad\u03c2 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b1 tokens \u03b1\u03c0\u03cc \u03c4\u03b1 SFT. \u03a4\u03bf \u03b6\u03b7\u03c4\u03bf\u03cd\u03bc\u03b5\u03bd\u03bf \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03bd \u03ad\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b3\u03c1\u03ac\u03c6\u03b5\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf, \u03b1\u03bb\u03bb\u03ac \u03b1\u03bd \u03ba\u03b1\u03c4\u03b1\u03bd\u03ad\u03bc\u03b5\u03b9 \u03bc\u03b5 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc \u03c4\u03c1\u03cc\u03c0\u03bf \u03c4\u03bf computational effort \u03cc\u03c4\u03b1\u03bd \u03b2\u03bb\u03ad\u03c0\u03b5\u03b9 \u03be\u03b1\u03bd\u03ac \u03c4\u03bf \u03af\u03b4\u03b9\u03bf \u03c0\u03c1\u03cc\u03b2\u03bb\u03b7\u03bc\u03b1.<\/p>\n<p>\u03a4\u03bf DeepSeek-Math-RL \u03ad\u03c6\u03c4\u03b1\u03c3\u03b5 CV \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 1,3 \u03c3\u03c4\u03b7 \u03b6\u03ce\u03bd\u03b7 accuracy 40%\u201360%, \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 0,95 \u03c3\u03c4\u03b7 \u03b6\u03ce\u03bd\u03b7 0%\u201320% \u03ba\u03b1\u03b9 \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 0,35 \u03c3\u03c4\u03b7 \u03b6\u03ce\u03bd\u03b7 80%\u2013100%. \u03a4\u03bf DeepSeek-Math-Instruct \u03b5\u03af\u03c7\u03b5 \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03b7 \u03bc\u03b5\u03c4\u03b1\u03b2\u03bb\u03b7\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1. \u0391\u03bd\u03c4\u03af\u03b8\u03b5\u03c4\u03b1, \u03c4\u03b1 Olmo-3-Think \u03ba\u03b1\u03b9 Olmo-3-Instruct \u03b4\u03b9\u03b1\u03c4\u03ae\u03c1\u03b7\u03c3\u03b1\u03bd CV 0,1\u20130,125 \u03cc\u03c0\u03bf\u03c5 \u03c5\u03c0\u03ae\u03c1\u03c7\u03b1\u03bd \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1.<\/p>\n<p>\u0386\u03c1\u03b1 \u03c4\u03bf RL \u03b4\u03b5\u03bd \u03b5\u03b3\u03b3\u03c5\u03ac\u03c4\u03b1\u03b9 \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c8\u03b9\u03bc\u03b7 adaptive compute allocation. Reward structure, \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1, decoding \u03ba\u03b1\u03b9 \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03bf \u03c4\u03bf pipeline \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03c5\u03c0\u03b5\u03c1\u03b9\u03c3\u03c7\u03cd\u03c3\u03bf\u03c5\u03bd \u03c4\u03b7\u03c2 \u03b5\u03c4\u03b9\u03ba\u03ad\u03c4\u03b1\u03c2 \u03b5\u03ba\u03c0\u03b1\u03af\u03b4\u03b5\u03c5\u03c3\u03b7\u03c2. \u03a3\u03b5 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03b9\u03ba\u03ac \u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1, \u03b1\u03c5\u03c4\u03cc \u03c3\u03c5\u03bd\u03b4\u03ad\u03b5\u03c4\u03b1\u03b9 \u03ac\u03bc\u03b5\u03c3\u03b1 \u03bc\u03b5 <a href=\"https:\/\/twodots.gr\/llm-routing-latency-accuracy-cost\/\">routing \u03c0\u03bf\u03c5 \u03c3\u03c5\u03bd\u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03af\u03b6\u03b5\u03b9 latency, accuracy \u03ba\u03b1\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2<\/a>.<\/p>\n<h2 id=\"arithmitiko-keno-paper\">\u03a4\u03bf \u03b1\u03c1\u03b9\u03b8\u03bc\u03b7\u03c4\u03b9\u03ba\u03cc \u03ba\u03b5\u03bd\u03cc \u03c0\u03bf\u03c5 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c0\u03c1\u03bf\u03c3\u03bf\u03c7\u03ae<\/h2>\n<p>\u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03b3\u03c1\u03ac\u03c6\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03ae\u03b8\u03b7\u03ba\u03b1\u03bd 50 \u03c0\u03c1\u03bf\u03b2\u03bb\u03ae\u03bc\u03b1\u03c4\u03b1, \u03bc\u03b5 50 \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03b5\u03c2 \u03b1\u03c0\u03b1\u03bd\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2 \u03b1\u03bd\u03ac \u03c0\u03c1\u03cc\u03b2\u03bb\u03b7\u03bc\u03b1 \u03ba\u03b1\u03b9 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf. \u0391\u03c5\u03c4\u03cc \u03b1\u03bd\u03c4\u03b9\u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03af \u03c3\u03b5 2.500 responses \u03b1\u03bd\u03ac model. \u03a3\u03c4\u03b7\u03bd \u03af\u03b4\u03b9\u03b1 \u03c0\u03b1\u03c1\u03ac\u03b3\u03c1\u03b1\u03c6\u03bf \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03c4\u03b1\u03b9 \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf 15.000 responses \u03b1\u03bd\u03ac model, \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc\u03c2 \u03c0\u03bf\u03c5 \u03b4\u03b5\u03bd \u03c0\u03c1\u03bf\u03ba\u03cd\u03c0\u03c4\u03b5\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf\u03bd \u03b4\u03b7\u03bb\u03c9\u03bc\u03ad\u03bd\u03bf \u03c0\u03bf\u03bb\u03bb\u03b1\u03c0\u03bb\u03b1\u03c3\u03b9\u03b1\u03c3\u03bc\u03cc.<\/p>\n<p>\u0393\u03b9\u03b1 \u03b1\u03c5\u03c4\u03cc \u03c4\u03bf \u03ac\u03c1\u03b8\u03c1\u03bf \u03ba\u03c1\u03b1\u03c4\u03ac \u03c4\u03bf \u03b5\u03c0\u03b1\u03bb\u03b7\u03b8\u03b5\u03cd\u03c3\u03b9\u03bc\u03bf \u03c0\u03c1\u03c9\u03c4\u03cc\u03ba\u03bf\u03bb\u03bb\u03bf \u03c4\u03c9\u03bd 50 \u00d7 50 \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03c4\u03bf \u03b1\u03c3\u03cd\u03bc\u03b2\u03b1\u03c4\u03bf \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf \u03c9\u03c2 metric. \u0397 \u03b1\u03c0\u03cc\u03ba\u03bb\u03b9\u03c3\u03b7 \u03b4\u03b5\u03bd \u03b1\u03ba\u03c5\u03c1\u03ce\u03bd\u03b5\u03b9 \u03b1\u03c5\u03c4\u03bf\u03bc\u03ac\u03c4\u03c9\u03c2 \u03c4\u03b1 \u03b3\u03c1\u03b1\u03c6\u03ae\u03bc\u03b1\u03c4\u03b1, \u03b1\u03bb\u03bb\u03ac \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c4\u03b7 \u03b2\u03b5\u03b2\u03b1\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b3\u03b9\u03b1 \u03c4\u03bf \u03b1\u03ba\u03c1\u03b9\u03b2\u03ad\u03c2 \u03c0\u03bb\u03ae\u03b8\u03bf\u03c2 \u03b5\u03ba\u03c4\u03b5\u03bb\u03ad\u03c3\u03b5\u03c9\u03bd \u03bc\u03ad\u03c7\u03c1\u03b9 \u03bd\u03b1 \u03c5\u03c0\u03ac\u03c1\u03be\u03b5\u03b9 \u03b4\u03b9\u03cc\u03c1\u03b8\u03c9\u03c3\u03b7 \u03ae \u03b5\u03c0\u03b9\u03c0\u03bb\u03ad\u03bf\u03bd \u03c4\u03b5\u03ba\u03bc\u03b7\u03c1\u03af\u03c9\u03c3\u03b7 \u03b1\u03c0\u03cc \u03c4\u03bf\u03c5\u03c2 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2.<\/p>\n<p>\u0391\u03c5\u03c4\u03cc \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03cc \u03bc\u03ac\u03b8\u03b7\u03bc\u03b1 \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 benchmark: \u03b5\u03bb\u03ad\u03b3\u03be\u03c4\u03b5 \u03b1\u03bd \u03c4\u03b1 totals \u03c3\u03c5\u03bc\u03c6\u03c9\u03bd\u03bf\u03cd\u03bd \u03bc\u03b5 \u03c4\u03bf sampling plan \u03ba\u03b1\u03b9 \u03b1\u03bd \u03bf\u03b9 \u03c0\u03af\u03bd\u03b1\u03ba\u03b5\u03c2 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03b1\u03c7\u03b8\u03bf\u03cd\u03bd. \u0397 <a href=\"https:\/\/twodots.gr\/every-eval-ever-ai-benchmarks-diafaneia\/\">\u03b4\u03b9\u03b1\u03c6\u03ac\u03bd\u03b5\u03b9\u03b1 \u03c3\u03c4\u03b1 AI benchmarks<\/a> \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03b7\u03c2 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2, \u03cc\u03c7\u03b9 \u03c4\u03c5\u03c0\u03b9\u03ba\u03ae \u03c5\u03c0\u03bf\u03c3\u03b7\u03bc\u03b5\u03af\u03c9\u03c3\u03b7.<\/p>\n<div class=\"td-decision-band\">\n<p class=\"td-decision-label\">\u039a\u03b1\u03bd\u03cc\u03bd\u03b1\u03c2 \u03b1\u03bd\u03ac\u03b3\u03bd\u03c9\u03c3\u03b7\u03c2 benchmark<\/p>\n<p><strong>\u038c\u03c4\u03b1\u03bd \u03c4\u03bf \u03b1\u03bd\u03b1\u03c6\u03b5\u03c1\u03cc\u03bc\u03b5\u03bd\u03bf \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf \u03b4\u03b5\u03bd \u03c3\u03c5\u03bc\u03c6\u03c9\u03bd\u03b5\u03af \u03bc\u03b5 \u03c4\u03bf \u03c0\u03c1\u03c9\u03c4\u03cc\u03ba\u03bf\u03bb\u03bb\u03bf, \u03b4\u03b7\u03bc\u03bf\u03c3\u03b9\u03b5\u03cd\u03c3\u03c4\u03b5 \u03c4\u03b7 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b7\u03c1\u03af\u03be\u03c4\u03b5 \u03c4\u03bf \u03c3\u03c5\u03bc\u03c0\u03ad\u03c1\u03b1\u03c3\u03bc\u03b1 \u03bc\u03cc\u03bd\u03bf \u03c3\u03c4\u03b1 \u03b5\u03c0\u03b1\u03bb\u03b7\u03b8\u03b5\u03cd\u03c3\u03b9\u03bc\u03b1 \u03bc\u03b5\u03b3\u03ad\u03b8\u03b7.<\/strong><\/p>\n<p>\u0397 \u03c0\u03c1\u03bf\u03c3\u03b5\u03ba\u03c4\u03b9\u03ba\u03ae \u03c4\u03b5\u03ba\u03bc\u03b7\u03c1\u03af\u03c9\u03c3\u03b7 \u03c0\u03c1\u03bf\u03c3\u03c4\u03b1\u03c4\u03b5\u03cd\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03b1\u03c0\u03cc \u03b5\u03bd\u03c4\u03c5\u03c0\u03c9\u03c3\u03b9\u03b1\u03ba\u03bf\u03cd\u03c2 \u03b1\u03bb\u03bb\u03ac \u03b1\u03c3\u03cd\u03bc\u03b2\u03b1\u03c4\u03bf\u03c5\u03c2 \u03b1\u03c1\u03b9\u03b8\u03bc\u03bf\u03cd\u03c2.<\/p>\n<\/div>\n<h2 id=\"ai-procurement-simasia\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03bf\u03c5\u03bd \u03c4\u03b1 \u03b5\u03c5\u03c1\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b3\u03b9\u03b1 AI procurement<\/h2>\n<p>\u0393\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03bf\u03bc\u03ac\u03b4\u03b1 e-commerce, marketing \u03ae customer support, \u03c4\u03bf \u03c3\u03c5\u03bc\u03c0\u03ad\u03c1\u03b1\u03c3\u03bc\u03b1 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03bd\u03b1 \u03b6\u03b7\u03c4\u03ae\u03c3\u03b5\u03b9 hidden-state probes \u03c0\u03c1\u03b9\u03bd \u03b1\u03b3\u03bf\u03c1\u03ac\u03c3\u03b5\u03b9 \u03ad\u03bd\u03b1 \u03b5\u03c1\u03b3\u03b1\u03bb\u03b5\u03af\u03bf. \u0395\u03af\u03bd\u03b1\u03b9 \u03bd\u03b1 \u03bc\u03b7\u03bd \u03b1\u03bd\u03c4\u03b9\u03bc\u03b5\u03c4\u03c9\u03c0\u03af\u03b6\u03b5\u03b9 \u03c4\u03bf benchmark score \u03c9\u03c2 \u03c0\u03bb\u03ae\u03c1\u03b7 \u03c0\u03b5\u03c1\u03b9\u03b3\u03c1\u03b1\u03c6\u03ae \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2. \u0388\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c0\u03b5\u03c4\u03c5\u03c7\u03b1\u03af\u03bd\u03b5\u03b9 \u03c5\u03c8\u03b7\u03bb\u03ae accuracy, \u03b1\u03bb\u03bb\u03ac \u03bd\u03b1 \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03b9\u03ac\u03b6\u03b5\u03b9 \u03bc\u03b5\u03b3\u03ac\u03bb\u03b7 \u03b4\u03b9\u03b1\u03ba\u03cd\u03bc\u03b1\u03bd\u03c3\u03b7 \u03c3\u03b5 latency, output length \u03ba\u03b1\u03b9 token cost.<\/p>\n<p>\u0397 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03bc\u03bf\u03bd\u03ac\u03b4\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf model version \u03bc\u03b1\u03b6\u03af \u03bc\u03b5 prompt, sampling settings, \u03b5\u03c1\u03b3\u03b1\u03bb\u03b5\u03af\u03b1 \u03ba\u03b1\u03b9 deployment stack. \u039f\u03b9 \u03cc\u03c1\u03bf\u03b9 RL, SFT, Thinking \u03ba\u03b1\u03b9 Instruct \u03b5\u03af\u03bd\u03b1\u03b9 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b5\u03c2 \u03b5\u03c4\u03b9\u03ba\u03ad\u03c4\u03b5\u03c2 \u03c0\u03c1\u03bf\u03ad\u03bb\u03b5\u03c5\u03c3\u03b7\u03c2, \u03cc\u03c7\u03b9 \u03b5\u03b3\u03b3\u03cd\u03b7\u03c3\u03b7 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac\u03c2. \u0391\u03c5\u03c4\u03cc\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03b1\u03b9 \u03bf \u03bb\u03cc\u03b3\u03bf\u03c2 \u03c0\u03bf\u03c5 <a href=\"https:\/\/twodots.gr\/poio-ai-montelo-axizei-gia-tin-epicheirisi\/\">\u03ba\u03b1\u03bd\u03ad\u03bd\u03b1 AI \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b4\u03b5\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03bc\u03cc\u03bd\u03b9\u03bc\u03bf \u03c3\u03c4\u03bf\u03af\u03c7\u03b7\u03bc\u03b1<\/a> \u03c7\u03c9\u03c1\u03af\u03c2 \u03c3\u03c5\u03bd\u03b5\u03c7\u03ae \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7.<\/p>\n<p>\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03b1\u03bc\u03b2\u03b1\u03bd\u03cc\u03bc\u03b5\u03bd\u03b1 runs \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf input, \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac test cases. \u03a7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03c4\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac tasks: \u03ba\u03b1\u03c4\u03b7\u03b3\u03bf\u03c1\u03b9\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03b1\u03b9\u03c4\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd, \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf \u03c0\u03bf\u03bb\u03b9\u03c4\u03b9\u03ba\u03ce\u03bd, \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03af\u03b1 briefs, \u03c3\u03cd\u03bd\u03bf\u03c8\u03b7 \u03c0\u03b1\u03c1\u03b1\u03b3\u03b3\u03b5\u03bb\u03b9\u03ce\u03bd \u03ae \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03ba\u03b1\u03c4\u03b1\u03bb\u03cc\u03b3\u03bf\u03c5. \u0397 \u03bc\u03b1\u03b8\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03b1 findings \u03bc\u03b5\u03c4\u03b1\u03c6\u03ad\u03c1\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c5\u03c4\u03bf\u03cd\u03c3\u03b9\u03b1 \u03c3\u03b5 \u03b1\u03c5\u03c4\u03ac \u03c4\u03b1 \u03c0\u03b5\u03b4\u03af\u03b1, \u03b1\u03bb\u03bb\u03ac \u03b4\u03af\u03bd\u03b5\u03b9 \u03b9\u03c3\u03c7\u03c5\u03c1\u03ae \u03bc\u03b5\u03b8\u03bf\u03b4\u03bf\u03bb\u03bf\u03b3\u03b9\u03ba\u03ae \u03ba\u03b1\u03c4\u03b5\u03cd\u03b8\u03c5\u03bd\u03c3\u03b7.<\/p>\n<h2 id=\"axiologisi-reasoning-model-praktika\">\u03a0\u03ce\u03c2 \u03bd\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03ae\u03c3\u03b5\u03c4\u03b5 \u03ad\u03bd\u03b1 reasoning model \u03c3\u03c4\u03b7\u03bd \u03c0\u03c1\u03ac\u03be\u03b7<\/h2>\n<p>\u0397 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ae\u03c2 \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03b1\u03c3\u03af\u03b1 \u03c3\u03c5\u03bd\u03b4\u03c5\u03ac\u03b6\u03b5\u03b9 \u03b5\u03be\u03c9\u03c4\u03b5\u03c1\u03b9\u03ba\u03ae \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b9\u03ba\u03ae \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc\u03c4\u03b7\u03c4\u03b1. \u03a4\u03bf NIST AI RMF \u03bf\u03c1\u03b3\u03b1\u03bd\u03ce\u03bd\u03b5\u03b9 \u03c4\u03b7 \u03b4\u03b9\u03b1\u03c7\u03b5\u03af\u03c1\u03b9\u03c3\u03b7 \u03b3\u03cd\u03c1\u03c9 \u03b1\u03c0\u03cc Govern, Map, Measure \u03ba\u03b1\u03b9 Manage \u03ba\u03b1\u03b9 \u03c4\u03bf\u03bd\u03af\u03b6\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c3\u03c5\u03bd\u03b4\u03ad\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5 \u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc context \u03c7\u03c1\u03ae\u03c3\u03b7\u03c2, \u03c4\u03bf business value, \u03c4\u03b1 \u03cc\u03c1\u03b9\u03b1 \u03ba\u03b1\u03b9 \u03c4\u03bf human oversight.<\/p>\n<div class=\"td-step-list\">\n<p class=\"td-step-list-title\">\u0388\u03be\u03b9 \u03b2\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b3\u03b9\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 reasoning models \u03c3\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc workload<\/p>\n<ol>\n<li><span class=\"td-step-kicker\">Step 1<\/span><strong>\u039f\u03c1\u03af\u03c3\u03c4\u03b5 \u03c4\u03bf \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1<\/strong>\n<p>\u03a0\u03b5\u03c1\u03b9\u03b3\u03c1\u03ac\u03c8\u03c4\u03b5 \u03c4\u03bf task, \u03c4\u03bf \u03b1\u03c0\u03bf\u03b4\u03b5\u03ba\u03c4\u03cc \u03bb\u03ac\u03b8\u03bf\u03c2, \u03c4\u03bf SLA \u03ba\u03b1\u03b9 \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03bc\u03b9\u03b1\u03c2 \u03bb\u03b1\u03bd\u03b8\u03b1\u03c3\u03bc\u03ad\u03bd\u03b7\u03c2 \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7\u03c2 \u03c0\u03c1\u03b9\u03bd \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03c4\u03b5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 2<\/span><strong>\u039a\u03bb\u03b5\u03b9\u03b4\u03ce\u03c3\u03c4\u03b5 \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03b7 \u03c4\u03b7 \u03c3\u03c5\u03bd\u03c4\u03b1\u03b3\u03ae<\/strong>\n<p>\u039a\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c8\u03c4\u03b5 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ad\u03c2 model version, system prompt, \u03b5\u03c1\u03b3\u03b1\u03bb\u03b5\u03af\u03b1, temperature, top-p, \u03cc\u03c1\u03b9\u03b1 tokens \u03ba\u03b1\u03b9 \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ae \u03b5\u03ba\u03c4\u03ad\u03bb\u03b5\u03c3\u03b7\u03c2.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 3<\/span><strong>\u03a7\u03c9\u03c1\u03af\u03c3\u03c4\u03b5 selection \u03ba\u03b1\u03b9 reporting set<\/strong>\n<p>\u0395\u03c0\u03b9\u03bb\u03ad\u03be\u03c4\u03b5 \u03c1\u03c5\u03b8\u03bc\u03af\u03c3\u03b5\u03b9\u03c2 \u03c3\u03b5 \u03ad\u03bd\u03b1 \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf \u03ba\u03b1\u03b9 \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03c4\u03b5 \u03c4\u03bf \u03c4\u03b5\u03bb\u03b9\u03ba\u03cc \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03c3\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac, \u03b1\u03b8\u03ad\u03b1\u03c4\u03b1 \u03c0\u03b1\u03c1\u03b1\u03b4\u03b5\u03af\u03b3\u03bc\u03b1\u03c4\u03b1 \u03c4\u03bf\u03c5 \u03af\u03b4\u03b9\u03bf\u03c5 workload.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 4<\/span><strong>\u0395\u03c0\u03b1\u03bd\u03b1\u03bb\u03ac\u03b2\u03b5\u03c4\u03b5 \u03c4\u03bf \u03af\u03b4\u03b9\u03bf input<\/strong>\n<p>\u03a4\u03c1\u03ad\u03be\u03c4\u03b5 \u03ba\u03ac\u03b8\u03b5 \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03bf case \u03c0\u03bf\u03bb\u03bb\u03ad\u03c2 \u03c6\u03bf\u03c1\u03ad\u03c2 \u03ce\u03c3\u03c4\u03b5 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03c4\u03b5 \u03c4\u03b7 \u03b4\u03b9\u03b1\u03ba\u03cd\u03bc\u03b1\u03bd\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf \u03bc\u03b9\u03b1 \u03b5\u03c5\u03bd\u03bf\u03ca\u03ba\u03ae \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 5<\/span><strong>\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03af\u03b1 \u03bc\u03b1\u03b6\u03af<\/strong>\n<p>\u03a0\u03b1\u03c1\u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03ae\u03c3\u03c4\u03b5 accuracy, failure rate, latency, output tokens, \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03ba\u03b1\u03b9 coefficient of variation \u03b1\u03bd\u03ac \u03ba\u03b1\u03c4\u03b7\u03b3\u03bf\u03c1\u03af\u03b1 \u03b4\u03c5\u03c3\u03ba\u03bf\u03bb\u03af\u03b1\u03c2.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 6<\/span><strong>\u039f\u03c1\u03af\u03c3\u03c4\u03b5 go\/no-go \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b1\u03bd\u03ad\u03bb\u03b5\u03b3\u03c7\u03bf<\/strong>\n<p>\u0398\u03ad\u03c3\u03c4\u03b5 \u03cc\u03c1\u03b9\u03b1 \u03b1\u03c0\u03bf\u03b4\u03bf\u03c7\u03ae\u03c2, human review \u03b3\u03b9\u03b1 \u03c5\u03c8\u03b7\u03bb\u03cc \u03b1\u03bd\u03c4\u03af\u03ba\u03c4\u03c5\u03c0\u03bf \u03ba\u03b1\u03b9 \u03bd\u03ad\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03cc\u03c4\u03b1\u03bd \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf, prompt \u03ae deployment stack.<\/p>\n<\/li>\n<\/ol>\n<\/div>\n<p>\u0393\u03b9\u03b1 agentic workflows, \u03c0\u03c1\u03bf\u03c3\u03b8\u03ad\u03c3\u03c4\u03b5 \u03b4\u03b9\u03ba\u03b1\u03b9\u03ce\u03bc\u03b1\u03c4\u03b1 \u03b5\u03bb\u03ac\u03c7\u03b9\u03c3\u03c4\u03b7\u03c2 \u03c0\u03c1\u03cc\u03c3\u03b2\u03b1\u03c3\u03b7\u03c2, audit trail, fallback \u03ba\u03b1\u03b9 \u03b4\u03c5\u03bd\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c0\u03b1\u03cd\u03c3\u03b7\u03c2. \u038c\u03c4\u03b1\u03bd \u03c4\u03bf \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03bc\u03b1\u03b8\u03b1\u03af\u03bd\u03b5\u03b9 \u03ae \u03b5\u03bd\u03b5\u03c1\u03b3\u03b5\u03af \u03bc\u03b5 \u03c0\u03bf\u03bb\u03b9\u03c4\u03b9\u03ba\u03ad\u03c2 reinforcement learning, \u03b7 <a href=\"https:\/\/twodots.gr\/epalithefsi-politikon-reinforcement-learning-ai-agent\/\">\u03b5\u03c0\u03b1\u03bb\u03ae\u03b8\u03b5\u03c5\u03c3\u03b7 \u03c4\u03b7\u03c2 \u03c0\u03bf\u03bb\u03b9\u03c4\u03b9\u03ba\u03ae\u03c2 \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae<\/a> \u03b5\u03af\u03bd\u03b1\u03b9 \u03be\u03b5\u03c7\u03c9\u03c1\u03b9\u03c3\u03c4\u03cc gate \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03bc\u03b9\u03b1\u03c2 \u03bc\u03b5\u03bc\u03bf\u03bd\u03c9\u03bc\u03ad\u03bd\u03b7\u03c2 \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7\u03c2.<\/p>\n<h2 id=\"oria-ergasias\">\u03a4\u03b9 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03b7 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1<\/h2>\n<p>\u03a4\u03b1 probes \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c1\u03ba\u03b5\u03c4\u03ac \u03b9\u03c3\u03bf\u03c1\u03c1\u03bf\u03c0\u03b7\u03bc\u03ad\u03bd\u03b5\u03c2 \u03c3\u03c9\u03c3\u03c4\u03ad\u03c2 \u03ba\u03b1\u03b9 \u03bb\u03b1\u03bd\u03b8\u03b1\u03c3\u03bc\u03ad\u03bd\u03b5\u03c2 \u03b1\u03c0\u03b1\u03bd\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2. \u0391\u03bd \u03ad\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b1\u03c0\u03b1\u03bd\u03c4\u03ac \u03c3\u03c7\u03b5\u03b4\u03cc\u03bd \u03c0\u03ac\u03bd\u03c4\u03b1 \u03c3\u03c9\u03c3\u03c4\u03ac \u03ae \u03c3\u03c7\u03b5\u03b4\u03cc\u03bd \u03c0\u03ac\u03bd\u03c4\u03b1 \u03bb\u03ac\u03b8\u03bf\u03c2, \u03bf classifier \u03ba\u03b9\u03bd\u03b4\u03c5\u03bd\u03b5\u03cd\u03b5\u03b9 \u03bd\u03b1 \u03bc\u03ac\u03b8\u03b5\u03b9 \u03c4\u03b7\u03bd \u03c0\u03bb\u03b5\u03b9\u03bf\u03c8\u03b7\u03c6\u03b9\u03ba\u03ae \u03ba\u03bb\u03ac\u03c3\u03b7. \u03a4\u03b1 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03ac\u03c3\u03bc\u03b1\u03c4\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b5\u03c0\u03bf\u03bc\u03ad\u03bd\u03c9\u03c2 \u03c0\u03b9\u03bf \u03b1\u03be\u03b9\u03cc\u03c0\u03b9\u03c3\u03c4\u03b1 \u03ba\u03bf\u03bd\u03c4\u03ac \u03c3\u03c4\u03b1 \u03cc\u03c1\u03b9\u03b1 \u03b9\u03ba\u03b1\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03c4\u03c9\u03bd \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03c9\u03bd.<\/p>\n<p>\u0397 mean-ablation \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03b5\u03b9 \u03b4\u03cd\u03bf DeepSeek \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03ba\u03b1\u03b9 \u03bc\u03cc\u03bd\u03bf 20 \u03c0\u03c1\u03bf\u03b2\u03bb\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b1\u03bd\u03ac \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf. \u03a4\u03bf probing \u03b5\u03c3\u03c4\u03b9\u03ac\u03b6\u03b5\u03b9 \u03c3\u03b5 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03b1 \u03bc\u03b1\u03b8\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac templates, \u03cc\u03c7\u03b9 \u03c3\u03b5 code generation, customer support \u03ae \u03b5\u03bc\u03c0\u03bf\u03c1\u03b9\u03ba\u03ac workflows. \u0395\u03c0\u03af\u03c3\u03b7\u03c2, \u03ad\u03bd\u03b1 extractable signal \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03cc \u03bc\u03cc\u03bd\u03bf \u03c4\u03bf\u03c5 \u03b1\u03b9\u03c4\u03b9\u03b1\u03ba\u03ae \u03b1\u03c0\u03cc\u03b4\u03b5\u03b9\u03be\u03b7.<\/p>\n<p>\u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03c0\u03c1\u03bf\u03c4\u03b5\u03af\u03bd\u03bf\u03c5\u03bd \u03b5\u03c0\u03ad\u03ba\u03c4\u03b1\u03c3\u03b7 \u03c3\u03b5 code generation \u03ba\u03b1\u03b9 scientific reasoning, probing \u03b5\u03bd\u03b4\u03b9\u03ac\u03bc\u03b5\u03c3\u03c9\u03bd \u03b2\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd \u03ba\u03b1\u03b9 real-time \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 representation quality. \u0391\u03c5\u03c4\u03ac \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03b5\u03bb\u03bb\u03bf\u03bd\u03c4\u03b9\u03ba\u03ad\u03c2 \u03ba\u03b1\u03c4\u03b5\u03c5\u03b8\u03cd\u03bd\u03c3\u03b5\u03b9\u03c2. \u0394\u03b5\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03bf\u03cd\u03bd \u03c9\u03c2 \u03ae\u03b4\u03b7 \u03b5\u03c0\u03b1\u03bb\u03b7\u03b8\u03b5\u03c5\u03bc\u03ad\u03bd\u03b1 \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd\u03c4\u03b1 monitoring.<\/p>\n<h2 id=\"asfales-epicheirimatiko-symperasma\">\u03a4\u03bf \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03c3\u03c5\u03bc\u03c0\u03ad\u03c1\u03b1\u03c3\u03bc\u03b1<\/h2>\n<p>\u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03c0\u03c1\u03bf\u03c3\u03c6\u03ad\u03c1\u03b5\u03b9 \u03b4\u03cd\u03bf \u03c3\u03c5\u03b3\u03ba\u03bb\u03af\u03bd\u03bf\u03c5\u03c3\u03b5\u03c2 \u03b5\u03bd\u03b4\u03b5\u03af\u03be\u03b5\u03b9\u03c2 \u03cc\u03c4\u03b9 \u03b7 reasoning-oriented \u03b5\u03ba\u03c0\u03b1\u03af\u03b4\u03b5\u03c5\u03c3\u03b7 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03bd\u03b1\u03b4\u03bf\u03bc\u03b5\u03af \u03c4\u03bf\u03bd \u03b5\u03c3\u03c9\u03c4\u03b5\u03c1\u03b9\u03ba\u03cc \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03bc\u03cc: \u03ba\u03b1\u03b8\u03b1\u03c1\u03cc\u03c4\u03b5\u03c1\u03b1 \u03ba\u03b1\u03b9 \u03bd\u03c9\u03c1\u03af\u03c4\u03b5\u03c1\u03b1 representations \u03c4\u03b7\u03c2 \u03bf\u03c1\u03b8\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2, \u03bc\u03b1\u03b6\u03af \u03bc\u03b5 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b9\u03ba\u03ae \u03b2\u03b1\u03c1\u03cd\u03c4\u03b7\u03c4\u03b1 \u03c3\u03c4\u03b1 \u03b2\u03b1\u03b8\u03cd\u03c4\u03b5\u03c1\u03b1 layers. \u03a4\u03b1\u03c5\u03c4\u03cc\u03c7\u03c1\u03bf\u03bd\u03b1, \u03b1\u03c0\u03bf\u03c1\u03c1\u03af\u03c0\u03c4\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b9\u03b4\u03ad\u03b1 \u03cc\u03c4\u03b9 \u03c4\u03bf RL \u03b5\u03b3\u03b3\u03c5\u03ac\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ae \u03c7\u03c1\u03ae\u03c3\u03b7 tokens.<\/p>\n<p>\u0393\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7, \u03b7 \u03ce\u03c1\u03b9\u03bc\u03b7 \u03b1\u03c1\u03c7\u03ae \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03bb\u03ae: \u03bc\u03b7\u03bd \u03b1\u03b3\u03bf\u03c1\u03ac\u03b6\u03b5\u03c4\u03b5 training label. \u0391\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03ae\u03c3\u03c4\u03b5 \u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03c3\u03c4\u03b9\u03c2 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c3\u03c5\u03bd\u03b8\u03ae\u03ba\u03b5\u03c2 \u03c3\u03b1\u03c2 \u03ba\u03b1\u03b9 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf \u03b1\u03bd \u03c0\u03ad\u03c4\u03c5\u03c7\u03b5, \u03b1\u03bb\u03bb\u03ac \u03c0\u03cc\u03c3\u03bf \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ac, \u03c0\u03cc\u03c3\u03bf \u03b3\u03c1\u03ae\u03b3\u03bf\u03c1\u03b1 \u03ba\u03b1\u03b9 \u03bc\u03b5 \u03c0\u03cc\u03c3\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03c0\u03ad\u03c4\u03c5\u03c7\u03b5. \u0397 \u03b5\u03c3\u03c9\u03c4\u03b5\u03c1\u03b9\u03ba\u03ae \u03b5\u03c1\u03bc\u03b7\u03bd\u03b5\u03c5\u03c3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03bf\u03bb\u03cd\u03c4\u03b9\u03bc\u03b7 \u03ad\u03c1\u03b5\u03c5\u03bd\u03b1\u00b7 \u03b7 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7, \u03cc\u03bc\u03c9\u03c2, \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03ae\u03c8\u03b9\u03bc\u03b7 \u03b5\u03be\u03c9\u03c4\u03b5\u03c1\u03b9\u03ba\u03ae \u03b1\u03c0\u03cc\u03b4\u03b5\u03b9\u03be\u03b7.<\/p>\n<section class=\"td-service-cta\">\n<div class=\"td-service-cta-content\">\n<p class=\"td-service-cta-eyebrow\">Business automation and AI from TWO DOTS<\/p>\n<p class=\"td-service-cta-title\">\u0395\u03c0\u03b9\u03bb\u03ad\u03be\u03c4\u03b5 reasoning model \u03bc\u03b5 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03b1\u03c0\u03cc \u03c4\u03bf \u03b4\u03b9\u03ba\u03cc \u03c3\u03b1\u03c2 workload.<\/p>\n<p>\u0397 TWO DOTS \u03c3\u03c7\u03b5\u03b4\u03b9\u03ac\u03b6\u03b5\u03b9 AI \u03b1\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2 \u03bc\u03b5 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03b1 test sets, \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03b1\u03bc\u03b2\u03b1\u03bd\u03cc\u03bc\u03b5\u03bd\u03b1 runs, \u03c0\u03b1\u03c1\u03b1\u03ba\u03bf\u03bb\u03bf\u03cd\u03b8\u03b7\u03c3\u03b7 latency \u03ba\u03b1\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c5\u03c2, validation gates \u03ba\u03b1\u03b9 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b7 \u03ad\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03cc\u03c0\u03bf\u03c5 \u03c4\u03bf \u03c1\u03af\u03c3\u03ba\u03bf \u03c4\u03bf \u03b1\u03c0\u03b1\u03b9\u03c4\u03b5\u03af.<\/p>\n<div class=\"td-service-cta-actions\"><a class=\"td-service-cta-button\" href=\"https:\/\/twodots.gr\/aftomatismoi-epicheiriseon-ai\/\">\u03a3\u03c7\u03b5\u03b4\u03b9\u03ac\u03c3\u03c4\u03b5 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf AI workflow<\/a><\/div>\n<\/div>\n<\/section>\n<section id=\"sychnes-erotiseis\" class=\"td-faq-section\">\n<div class=\"td-faq\">\n<p class=\"td-faq-heading\">Frequently Asked Questions (FAQs)<\/p>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03bf\u03b9\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b7 \u03b2\u03b1\u03c3\u03b9\u03ba\u03ae \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac RL \u03ba\u03b1\u03b9 SFT \u03c3\u03c4\u03b7 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a4\u03bf SFT \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03cd\u03b5\u03b9 \u03c0\u03ac\u03bd\u03c9 \u03c3\u03b5 \u03c0\u03b1\u03c1\u03b1\u03b4\u03b5\u03af\u03b3\u03bc\u03b1\u03c4\u03b1 \u03b5\u03c0\u03b9\u03b8\u03c5\u03bc\u03b7\u03c4\u03ce\u03bd \u03b1\u03c0\u03b1\u03bd\u03c4\u03ae\u03c3\u03b5\u03c9\u03bd, \u03b5\u03bd\u03ce \u03bf\u03b9 RL-oriented \u03c0\u03b1\u03c1\u03b1\u03bb\u03bb\u03b1\u03b3\u03ad\u03c2 \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03bc\u03b5 reward signals. \u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03b9 \u03c4\u03b9\u03c2 \u03b5\u03c3\u03c9\u03c4\u03b5\u03c1\u03b9\u03ba\u03ad\u03c2 \u03ba\u03b1\u03b9 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03b9\u03ba\u03ad\u03c2 \u03c3\u03c5\u03bd\u03ad\u03c0\u03b5\u03b9\u03b5\u03c2 \u03b1\u03c5\u03c4\u03ce\u03bd \u03c4\u03c9\u03bd pipelines.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03bc\u03b5\u03c4\u03c1\u03ac \u03ad\u03bd\u03b1 linear probe;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u039c\u03b5\u03c4\u03c1\u03ac \u03c0\u03cc\u03c3\u03bf \u03b5\u03cd\u03ba\u03bf\u03bb\u03b1 \u03ad\u03bd\u03b1\u03c2 \u03b1\u03c0\u03bb\u03cc\u03c2 \u03b3\u03c1\u03b1\u03bc\u03bc\u03b9\u03ba\u03cc\u03c2 classifier \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03be\u03b5\u03c7\u03c9\u03c1\u03af\u03c3\u03b5\u03b9 \u03c3\u03c9\u03c3\u03c4\u03ad\u03c2 \u03b1\u03c0\u03cc \u03bb\u03b1\u03bd\u03b8\u03b1\u03c3\u03bc\u03ad\u03bd\u03b5\u03c2 \u03b1\u03c0\u03b1\u03bd\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ce\u03bd\u03c4\u03b1\u03c2 \u03c4\u03bf hidden state \u03b5\u03bd\u03cc\u03c2 layer.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0397 \u03c5\u03c8\u03b7\u03bb\u03ae probe accuracy \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03b1\u03b9\u03c4\u03b9\u03b1\u03ba\u03cc reasoning;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9. \u0394\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03af\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 linearly separable \u03ba\u03b1\u03b9 \u03b5\u03be\u03b1\u03b3\u03ce\u03b3\u03b9\u03bc\u03b7, \u03cc\u03c7\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf probe \u03b1\u03c0\u03bf\u03ba\u03ac\u03bb\u03c5\u03c8\u03b5 \u03c4\u03bf\u03bd \u03b1\u03b9\u03c4\u03b9\u03b1\u03ba\u03cc \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03cc \u03c0\u03bf\u03c5 \u03b4\u03b7\u03bc\u03b9\u03bf\u03cd\u03c1\u03b3\u03b7\u03c3\u03b5 \u03c4\u03b7\u03bd \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b5 \u03b7 mean ablation \u03b3\u03b9\u03b1 \u03c4\u03b1 layers;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a3\u03c4\u03bf DeepSeek-Math-7B-RL \u03c4\u03b1 \u03b2\u03b1\u03b8\u03cd\u03c4\u03b5\u03c1\u03b1 layers \u03ad\u03b3\u03b9\u03bd\u03b1\u03bd \u03c0\u03c1\u03bf\u03bf\u03b4\u03b5\u03c5\u03c4\u03b9\u03ba\u03ac \u03c0\u03b9\u03bf \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03b1, \u03b5\u03bd\u03ce \u03c3\u03c4\u03bf DeepSeek-Math-7B-Instruct \u03b7 \u03c3\u03b7\u03bc\u03b1\u03c3\u03af\u03b1 \u03ae\u03c4\u03b1\u03bd \u03c0\u03b9\u03bf \u03bf\u03bc\u03bf\u03b9\u03cc\u03bc\u03bf\u03c1\u03c6\u03b1 \u03ba\u03b1\u03c4\u03b1\u03bd\u03b5\u03bc\u03b7\u03bc\u03ad\u03bd\u03b7.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b1 RL models \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03bd \u03c0\u03ac\u03bd\u03c4\u03b1 \u03c0\u03b9\u03bf \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ac \u03c4\u03b1 tokens;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9. \u03a4\u03bf DeepSeek-Math-RL \u03b5\u03af\u03c7\u03b5 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03bc\u03b5\u03c4\u03b1\u03b2\u03bb\u03b7\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b1\u03c0\u03cc \u03c4\u03bf Instruct, \u03b5\u03bd\u03ce \u03c4\u03b1 Olmo-3-Think \u03ba\u03b1\u03b9 Olmo-3-Instruct \u03b5\u03af\u03c7\u03b1\u03bd \u03c0\u03b1\u03c1\u03cc\u03bc\u03bf\u03b9\u03b1 \u03c7\u03b1\u03bc\u03b7\u03bb\u03ae \u03bc\u03b5\u03c4\u03b1\u03b2\u03bb\u03b7\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0393\u03b9\u03b1\u03c4\u03af \u03b4\u03b5\u03bd \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af\u03c4\u03b1\u03b9 \u03bf \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc\u03c2 15.000 responses \u03b1\u03bd\u03ac model;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a4\u03bf \u03c0\u03c1\u03c9\u03c4\u03cc\u03ba\u03bf\u03bb\u03bb\u03bf \u03b3\u03c1\u03ac\u03c6\u03b5\u03b9 50 \u03c0\u03c1\u03bf\u03b2\u03bb\u03ae\u03bc\u03b1\u03c4\u03b1 \u03ba\u03b1\u03b9 50 \u03b1\u03c0\u03b1\u03bd\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2 \u03b1\u03bd\u03ac \u03c0\u03c1\u03cc\u03b2\u03bb\u03b7\u03bc\u03b1, \u03b4\u03b7\u03bb\u03b1\u03b4\u03ae 2.500 \u03b1\u03c0\u03b1\u03bd\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2 \u03b1\u03bd\u03ac \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf. \u0395\u03c0\u03b5\u03b9\u03b4\u03ae \u03c4\u03bf \u03b1\u03bd\u03b1\u03c6\u03b5\u03c1\u03cc\u03bc\u03b5\u03bd\u03bf \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf 15.000 \u03b4\u03b5\u03bd \u03c3\u03c5\u03bc\u03c6\u03c9\u03bd\u03b5\u03af \u03bc\u03b5 \u03b1\u03c5\u03c4\u03cc\u03bd \u03c4\u03bf\u03bd \u03c0\u03bf\u03bb\u03bb\u03b1\u03c0\u03bb\u03b1\u03c3\u03b9\u03b1\u03c3\u03bc\u03cc, \u03c4\u03bf \u03ac\u03c1\u03b8\u03c1\u03bf \u03ba\u03c1\u03b1\u03c4\u03ac \u03c4\u03bf \u03c0\u03c1\u03c9\u03c4\u03cc\u03ba\u03bf\u03bb\u03bb\u03bf \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b9\u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b1\u03c3\u03c5\u03bd\u03ad\u03c0\u03b5\u03b9\u03b1.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u039c\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03c4\u03b1 \u03bc\u03b1\u03b8\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03b5\u03c5\u03c1\u03ae\u03bc\u03b1\u03c4\u03b1 \u03bd\u03b1 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03c3\u03c4\u03bf\u03cd\u03bd \u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 marketing \u03ae e-commerce;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9 \u03c9\u03c2 \u03ac\u03bc\u03b5\u03c3\u03b7 \u03b1\u03c0\u03cc\u03b4\u03b5\u03b9\u03be\u03b7 \u03b1\u03c0\u03cc\u03b4\u03bf\u03c3\u03b7\u03c2. \u0394\u03af\u03bd\u03bf\u03c5\u03bd \u03cc\u03bc\u03c9\u03c2 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b7 \u03bc\u03b5\u03b8\u03bf\u03b4\u03bf\u03bb\u03bf\u03b3\u03af\u03b1: \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac tasks, \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03ae\u03c8\u03b5\u03b9\u03c2 \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf input \u03ba\u03b1\u03b9 \u03ba\u03bf\u03b9\u03bd\u03ae \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2, latency, tokens \u03ba\u03b1\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c5\u03c2.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b6\u03b7\u03c4\u03ae\u03c3\u03b5\u03b9 \u03bc\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7 \u03c0\u03c1\u03b9\u03bd \u03b5\u03c0\u03b9\u03bb\u03ad\u03be\u03b5\u03b9 reasoning model;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0391\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03c3\u03c4\u03bf \u03b4\u03b9\u03ba\u03cc \u03c4\u03b7\u03c2 workload, \u03c3\u03b1\u03c6\u03ad\u03c2 model version, \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ad\u03c2 \u03c1\u03c5\u03b8\u03bc\u03af\u03c3\u03b5\u03b9\u03c2, \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03b1\u03bc\u03b2\u03b1\u03bd\u03cc\u03bc\u03b5\u03bd\u03b1 runs, failure modes \u03ba\u03b1\u03b9 \u03b4\u03b9\u03b1\u03ba\u03cd\u03bc\u03b1\u03bd\u03c3\u03b7 \u03c3\u03b5 latency \u03ba\u03b1\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2.<\/p>\n<\/div>\n<\/details>\n<\/div>\n<\/section>\n<div class=\"td-source-list\">\n<p id=\"piges\" class=\"td-source-list-title\">Sources<\/p>\n<ul>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2607.26119\" target=\"_blank\" rel=\"noopener\">Rahman et al. \u2014 Probing the Origins of Reasoning Performance: RL vs. SFT Fine-Tuned Models<\/a><\/li>\n<li><a href=\"https:\/\/oankit.github.io\/-rl-sft-reasoning\/\" target=\"_blank\" rel=\"noopener\">Algoverse AI Research \u2014 \u03b5\u03c0\u03af\u03c3\u03b7\u03bc\u03b7 \u03c3\u03b5\u03bb\u03af\u03b4\u03b1 \u03ad\u03c1\u03b3\u03bf\u03c5 \u03ba\u03b1\u03b9 \u03ba\u03ce\u03b4\u03b9\u03ba\u03b1\u03c2<\/a><\/li>\n<li><a href=\"https:\/\/airc.nist.gov\/airmf-resources\/airmf\/5-sec-core\/\" target=\"_blank\" rel=\"noopener\">NIST AI Risk Management Framework \u2014 Core: Govern, Map, Measure, Manage<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>\u0394\u03b5\u03af\u03c4\u03b5 \u03c4\u03b9 \u03b4\u03b5\u03af\u03c7\u03bd\u03bf\u03c5\u03bd linear probes, mean ablations \u03ba\u03b1\u03b9 token variability \u03b3\u03b9\u03b1 RL \u03ba\u03b1\u03b9 SFT reasoning models \u03ba\u03b1\u03b9 \u03c0\u03ce\u03c2 \u03bd\u03b1 \u03c4\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03b5\u03af\u03c4\u03b5 \u03c3\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac workloads.<\/p>","protected":false},"author":1,"featured_media":88055,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_gspb_post_css":"","content-type":"","footnotes":""},"categories":[199],"tags":[9261,7565,9225,7477,18034],"class_list":["post-87753","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-techniti-noimosyni","tag-ai-strategy","tag-artificial-intelligence-2","tag-llm","tag-machine-learning","tag-reinforcement-learning"],"blocksy_meta":[],"_links":{"self":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/posts\/87753","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/comments?post=87753"}],"version-history":[{"count":0,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/posts\/87753\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/media\/88055"}],"wp:attachment":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/media?parent=87753"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/categories?post=87753"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/tags?post=87753"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}