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Thinking Seeds: Restarting On-Policy RL from the Model's Own Past Reasoning States
On-policy reinforcement learning (RL) for language models trains on the reasoning states visited by the current policy. As training sharpens the policy, some states reached by earlier checkpoints disappear from its rollouts. We ask whether those states can be brought back without treating the action
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