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Gaussian guidance reveals that optimal trajectory retention depth varies across tasks, leading to a substantial performance boost in reinforcement learning.
LLMs can escape the trap of confidently wrong reasoning by co-evolving a generator and verifier from a single model, bootstrapping each other to break free from flawed consensus.
