Measuring Autonomous AI Research

Prime Intellect evaluated autonomous AI research by conducting 153 multi-day nanoGPT optimizer speedrun runs across 18 frontier models, using isolated 8xH200 GPU environments and validation procedures designed to limit chance results and rule violations. The benchmark asked agents to reduce the training steps needed for a 124M-parameter GPT model to reach a target validation loss, with the best result from Claude Fable 5 reaching 2,726 steps and closing 81.7% of the gap between the 3,290-step baseline and a 2,600-step human record claim. Leading models, including Fable 5, Opus 5, and Kimi K3, generally found known optimizer-related techniques rather than fundamentally new methods, but differed substantially in experimental design, noise estimation, multi-seed validation, re-testing, ablation practices, and development of reusable research tools. The study found that stronger agents were better at preserving weak signals, revisiting prior hypotheses as configurations changed, and using simulations or controlled tests to guide costly training experiments, while weaker models more often overinterpreted noisy single-run outcomes or implementation failures. The authors note considerable benchmark variance, limitations from limited replication and restricted internet access, and uncertainty about how well speedrun-derived methods transfer to practical model training, while releasing traces and proposing further work on multi-agent research systems and broader training benchmarks.

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