ParetoRSI-Gym: Can AI Agents Improve Language Models Across Multiple Domains?
ParetoRSI-Gym is a benchmark in which an agent repeatedly searches for, curates, and trains on data across five domains (math, code, finance, medicine, science) while the initial LLM, toolset, and SFT pipeline stay fixed. Held-out generalization benchmarks in each domain separate capability gains from overfitting to proxy scores. Across three agent systems, three base models, and five rounds, the strongest system (Claude Code with Opus-5) raises generalization scores by 9.2% relative (+2.3 absolute) on average. The authors report that the harness matters as much as the model and that each agent develops a distinct data selection strategy.
Open original source huggingface.coOne primary source; other links are context, not independent corroboration.
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ParetoRSI-Gym is a benchmark in which an agent repeatedly searches for, curates, and trains on data across five domains (math, code, finance, medicine, science) while the initial LLM, toolset, and SFT pipeline stay fixed. Held-out generalization benchmarks in each domain separate capability gains from overfitting to proxy scores. Across three agent systems, three base models, and five rounds, the strongest system (Claude Code with Opus-5) raises generalization scores by 9.2% relative (+2.3 absolute) on average. The authors report that the harness matters as much as the model and that each agent develops a distinct data selection strategy.
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- 04What could mislead?
Source claims not independently checked; results come from a single benchmark setup with a limited set of agent systems, base models, and rounds.
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Key findings from the stored brief
- The strongest system, Claude Code with Opus-5, raises proxy-best checkpoint generalization scores by 9.2% relative (+2.3 absolute) over the starting model.
- Single-domain relative gains reach up to 63%, with medicine on Ministral-3-3B as the cited example.
- The analysis finds the agent harness matters as much as the underlying model it drives.
- Each agent keeps a unique data selection strategy.
Source claims not independently checked; results come from a single benchmark setup with a limited set of agent systems, base models, and rounds.
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Why this may matter
Evaluating recursive self-improvement across multiple domains, with held-out generalization checks, is a step toward measuring whether agentic post-training yields real capability gains rather than benchmark overfitting in realistic machine learning engineering settings.
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