Unverified50% confidenceFactExact time
LM2LM periodically executes proposals that rank high under the current policy and are overestimated by the RWM, and adds the actual measurement results to the RWM's context to calibrate the world model.
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50%
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Long-term
Relevance
10/19/2026
First Seen
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Related Claims
UnverifiedThe LM2LM method trains an LLM via reinforcement learning to generate valid model improvement proposals, and uses a frozen LLM as a research world model (RWM) to predict proposal returns as the reward signal.79% similarUnverifiedLM2LM designs a human-machine collaborative feedback loop: the model proposes proposals, the world model screens them, and real execution calibrates them.75% similarUnverifiedThe core idea of RLMF is to optimize how accurately the model expresses its degree of confidence in an answer, rather than simply optimizing what the model says.73% similarUnverifiedIn terms of prediction accuracy, LJMs remain competitive with continuous-time latent baseline models.73% similarUnverifiedAutoregressive LLMs can already improve reasoning performance through chain-of-thought, RL fine-tuning, multiple sampling, and additional test-time compute.71% similar
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