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Recommended read. And I agree that the RSI is also a systems engineering problem. Self-improving agents need better research environments. RSIGym gives a research agent training, inference, evals, and sandboxes as services it can call. The agent spends its budget on experiments instead of rebuilding infra. With Opus 5 as the researcher, the improved system went from 17.67% to 50.33% on SWE-bench Verified. Also cool to see a way to measure the quality of co-evolution between harnesses and…
FanqingMengAI@FanqingMengAI16h ago
💡Our view: RSI is a systems engineering problem, not just a model problem. Progress depends on the environment a research agent works in: what resources it can call, what it can change, and how it runs experiments. That environment should reflect real production workflows and be easy for agents to use. At @Evolvent_AI, we built RSIGym around Everything as a Service. Agents can call established research services and iterate on data, training settings, and harness code. We also introduce RSI-Index to measure how well frontier agents jointly improve a target model’s weights and harness. Across 6 research agents and 5 benchmarks, Opus 5 leads at 0.4809. 🧵 We’re releasing the code, experiment configurations, research trajectories, and evaluation logs. Try your own improvement methods with RSIGym. 🌐 Website: https://t.co/vXiqt3YUN5 💻 GitHub: https://t.co/chjL41y9OH 📄 arXiv: https://t.co/UpDi0y6fIV