When Does Closed-Loop Curriculum Adaptation Help GRPO? An Analysis on Countdown
arXiv:2610.39056v1 Announce Type: cross Abstract: Reinforcement fine-tuning has become an effective post-training paradigm for improving the reasoning ability of large language models, but it remains sample-inefficient when the training distribution is mismatched to the model's current capability. In group-relative objectives such as Group Relative Policy Optimization (GRPO), problems that are too hard often produce uniformly failed rollouts, while problems that are too easy provide little usef…