Language models learn to improve other language models
In brief
- LM2LM uses reinforcement learning to train LLMs to propose model improvements.
- A research world model evaluates proposals at lower cost than real executions.
- The method cuts data-generation costs by 33% while boosting performance.
A new method, LM2LM, uses reinforcement learning to train language models to propose effective improvements for other models. It combines a research world model for low-cost evaluation and periodic updates with real data to ensure feedback reliability.
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