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LLM-as-a-Tutor: Policy-Aware Prompt Adaptation for Non-Verifiable RL
Reinforcement learning (RL) for non-verifiable instruction following increasingly relies on LLM judges with prompt-specific rubrics as reward signals. While recent methods adapt these rubrics to the evolving policy during training, the training prompts themselves remain static, drawn from fixed corp