Minhua Lin

Deciding which change is worth trying when a language model improves a language model has depended on costly training experiments. LM2LM uses a frozen LLM as a research world model that predicts a proposal's gain from real experimental records in context, turning those predictions into reinforcement-learning rewards, and periodically executes the current policy's top-rated proposals to write measured outcomes back into the records. Across eight autoregressive and diffusion language-model development environments, Qwen3.8-27B trained this way outperforms GPT-6-sol on held-out tasks, even when GPT-6-sol is given 300 real experimental records in context, at about 33% lower data-generation cost.

Deciding which change is worth trying when a language model improves a language model has depended on costly training experiments. LM2LM uses a frozen LLM as a research world model that predicts a proposal's gain from real experimental records in context, turning those predictions into reinforcement-learning rewards, and periodically executes the current policy's top-rated proposals to write measured outcomes back into the records. Across eight autoregressive and diffusion language-model development environments, Qwen3.8-27B trained this way outperforms GPT-6-sol on held-out tasks, even when GPT-6-sol is given 300 real experimental records in context, at about 33% lower data-generation cost.