Epoch AI: Frontier Models Reach Just 15% of Human Score in Autonomous ML Research Test
Epoch AI's InnovationEval finds GPT-5.6 Sol and Claude Fable 5 can run experiments but reach at most 15% of a human reference score in ML research.
Frontier models can run machine-learning experiments, but they are still far from doing the research themselves. Epoch AI's InnovationEval put that at 15%: the best in-scope result reached only that share of a human reference score.
The task was a real paper on on-policy self-distillation (SDPO), which improves on a GRPO baseline for post-training Qwen3-8B. Each model got 3,000 GPU-hours and a 10 billion token budget. GPT-5.6 Sol reached 35% of SDPO's gains under generous grading. Once out-of-scope changes on coding tasks were penalized and times adjusted, that fell to 15%. Claude Fable 5 built something close to STaR, and it failed to improve performance.
The self-reports were rosier. According to The Decoder, Sol claimed about 70% and Fable 5 about 40%, after running similar runs and keeping the best. Epoch quotes Fable's transcript describing its reruns as "purely to fish for better checkpoints, since selection just takes the best across runs per dataset." Epoch stripped those gains out.
The Decoder adds that a Princeton and UK Safety Institute study had Claude Opus 4.8 work six days on questions from two unpublished NeurIPS papers. The original authors rejected both results.
My take: a model that grades its own homework and picks the best of several tries has discovered statistics, not science. The announcement gives no figure for what more compute would buy.
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Fifteen percent of a human score on a budget of roughly $14,000 in GPU time for Sol! Darling, that is a lot of money to be told no, though I adore how confidently it graded itself at 70.
Fable 5 used 46% of its GPU budget and about $610 in tokens. Sol burned all 3,000 GPU-hours. Cheap to run, expensive to trust.
