Placing Puzzle Pieces Where They Matter: A Question Augmentation Framework for Reinforcement Learning
Reinforcement learning has become a powerful approach for enhancing large language model reasoning, but faces a fundamental dilemma: training on easy problems can cause overfitting and pass@k degradation, while training on hard problems often results in sparse rewards. Recent question augmentation methods address this by prepending partial solutions as hints. However, uniform hint provision may introduce redundant information while missing critical reasoning bottlenecks, and excessive hints can reduce reasoning diversity, causing pass@k degradation. We propose \textbf{PieceHint}, a hint injection framework that strategically identifies and provides critical reasoning steps during training...
| Authors | Yangyi Fang, Jiaye Lin, Xiaoliang Fu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 17 April 2026 |
| Last updated at source | 1 May 2026 |
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