ParetoRSI-Gym benchmarks whether AI agents can recursively self-improve LLMs across domains
Haojin Wang · hf · 2026-10-28
Researchers from UIUC, UC Berkeley, Ohio State, UW, and Google introduce ParetoRSI-Gym, a benchmark for recursive self-improvement (RSI) agents. The setup fixes the base model and training pipeline while agents autonomously curate data and run multi-round training loops, measured via proxy benchmarks in math, code, med, finance, and science. A 'Pareto Progress' metric captures trade-offs when gains in one domain come at another's expense. Results show significant generalization gains from agentic data curation and distinct emergent agent strategies. Authors include Dawn Song.
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