Recursive self-improvement of AI research agents
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TL;DR - AIDE² recursively improves an AI research agent by rewriting its own code, benchmarking each version, and retaining changes that perform best on hidden evaluations. An eight-day autonomous run produced transferable performance gains and unexpectedly reduced reward hacking.
- Discovered seven successive improvements, including a new search policy and mechanisms for compressing and managing growing context.
- The strongest self-improved agent matched or exceeded a leading human-engineered production agent across four held-out benchmarks.
- Gains transferred across machine learning engineering, heuristic algorithm engineering, and out-of-distribution weather forecasting.
- Reward hacking on a separate held-out task family fell from 55% to 32%, despite not being an explicit optimization target.
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Recursive self-improvement of AI research agents
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Hugging Face upvotes 16
TL;DR - AIDE² recursively improves an AI research agent by rewriting its own code, benchmarking each version, and retaining changes that perform best on hidden evaluations. An eight-day autonomous run produced transferable performance gains and unexpectedly reduced reward hacking.
- Discovered seven successive improvements, including a new search policy and mechanisms for compressing and managing growing context.
- The strongest self-improved agent matched or exceeded a leading human-engineered production agent across four held-out benchmarks.
- Gains transferred across machine learning engineering, heuristic algorithm engineering, and out-of-distribution weather forecasting.
- Reward hacking on a separate held-out task family fell from 55% to 32%, despite not being an explicit optimization target.