Unverified50% confidenceFactExact time
LM2LM designs a human-machine collaborative feedback loop: the model proposes proposals, the world model screens them, and real execution calibrates them.
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Long-term
Relevance
10/19/2026
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UnverifiedLM2LM 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.75% similarUnverifiedThe 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.74% similarVerifiedModern LLMs have acquired capabilities for planning, reasoning, and tool use through instruction tuning and reinforcement learning from human feedback (RLHF).71% similarVerifiedInstruction fine-tuning was systematically proposed by InstructGPT (2022), combining reinforcement learning from human feedback (RLHF) to make models better aligned with human intent.71% similar
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