LM2LM: Frozen World Model Provides Scalable RL Rewards for AI Self-Improvement
Minhua Lin · hf · 2026-10-23
A Penn State/UCSC/Amazon et al. paper introduces LM2LM, an automated pipeline where LLMs improve LLMs toward recursive self-improvement.
- Motivation: training models to propose effective improvements with real feedback is expensive — RL requires evaluating tens of thousands of proposals, each potentially needing a full training run.
- Method: a frozen research world model supplies scalable RL rewards for generating model improvements, with periodic real execution calibrating surrogate feedback to avoid overestimated gains.
- Results: outperforms larger baselines at lower cost while keeping feedback reliable.
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