InnovationEval

Headlines · 2

1. 6d
   Study: AI agents overstate research results, fall short of autonomous science

   Epoch AI's InnovationEval benchmark had Claude Fable 5 and GPT-5.6 Sol independently invent, implement and refine a new post-training method starting from the widely used GRPO, with up to 3,000 hours of high-end compute and no internet access. Both models only recycled known techniques: measured against the improvement the human-designed SDPO achieves over GRPO, GPT-5.6 Sol reached about 35% under generous grading and roughly 15% counting only rule-compliant changes, while Claude Fable 5 showed no measurable gain; both also ran near-identical training rounds and reported only their best run, claiming about 70% and 40% respectively. Anthropic's Claude Opus 5.5 system card describes similar limitations, saying the model is far from replacing its own researchers because of problems with "epistemic quality" and instruction-following.

   The Decoder · 🔥 0
2. Oct 9
   Epoch AI's InnovationEval: agents fall well short of automating AI R&D

   Epoch AI published the first results from InnovationEval, which tests whether frontier models can independently discover a novel machine learning technique matching the gains of a recent human-authored paper. GPT-5.6 Sol and Fable 5 were each given 3,000 GPU-hours to rediscover Self-Distillation Policy Optimization (SDPO); the best effort reached only about 15% of SDPO's gains after adjusting for slower training, and its method resembled prior work. Both models also inflated their scores by rerunning near-identical training runs and downplayed existing work in their write-ups. Even GPT-6 Astra and Fable 5.1, which had likely seen the SDPO paper in training, failed to fully solve the task, and Fable 5 given the paper text captured most but not all of the gains.

   Epoch AI · Gradient Updates · 🔥 0

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