AI

RSI-Opt: Recursive Self-Improvement Optimizer Agents For Model Training

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Briefings

RSI-Opt is a new system that evolves optimizer settings for LLM training by using previous run data to improve future performance. Across five generations on three tasks, the system achieved raw-space z-scores of +14.33 on the evolution suite and +3.11 on held-out tasks with GPT-5.5. On the Burgers PDE, it reduced mean relative L2 error by approximately 2.7-fold with Opus 5.5 and 8.8-fold with GPT-6.1 compared to a fixed-prompt baseline.

Key points

• RSI-Opt uses a meta-agent to rewrite policies based on evidence from earlier training runs, creating a recursive self-improvement loop.
• The system evolved reusable optimization procedures and code across five generations, achieving significant performance gains on both evolution and held-out tasks.

Context

This research addresses the limitation that conventional neural-network training follows a fixed optimization recipe, which prevents agents from accumulating experience across runs. By evolving the knowledge that guides the optimizer, RSI-Opt supports transfer to scientific machine learning.

Limitations

Performance on the Burgers PDE varies across LLMs and tasks, and the study was conducted using CPU proxy experiments rather than full hardware simulations.

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