Papers by Haolin Shi
Placing Puzzle Pieces Where They Matter: A Question Augmentation Framework for Reinforcement Learning (2026.acl-long)
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| Challenge: | Reinforcement learning (RL) training on easy problems can cause overfitting and pass@k degradation, while training on hard problems yields sparse reward signals. |
| Approach: | They propose a hint injection framework that strategically identifies and provides critical reasoning steps during training. |
| Outcome: | Experiments on six mathematical reasoning benchmarks show that the proposed framework achieves comparable average performance to 32B baselines while preserving pass@k diversity across all k values. |
How to Allocate, How to Learn? Dynamic Rollout Allocation and Advantage Modulation for Policy Optimization (2026.findings-acl)
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Yangyi Fang, Jiaye Lin, Xiaoliang Fu, Cong Qin, Haolin Shi, Chaowen Hu, Lu Pan, Ke Zeng, Xunliang Cai
| Challenge: | Existing methods for reinforcement learning with verifiable rewards are limited by the complexity of the problem and the complexity. |
| Approach: | They propose a theoretically-grounded dual-pronged optimization framework for reinforcement learning with verifiable rewards that compensates for gradient attenuation of high-confidence correct actions while utilizing entropy changes as computable indicators to stabilize excessive update magnitudes. |
| Outcome: | The proposed framework compensates for gradient attenuation of high-confidence correct actions while utilizing entropy changes as computable indicators to stabilize excessive update magnitudes. |
Proximity-Based Multi-Turn Optimization: Practical Credit Assignment for LLM Agent Training (2026.acl-industry)
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| Challenge: | Existing group-based policy optimization methods rely on statistical deviation within discrete batches, misallocating credit when task difficulty fluctuates. |
| Approach: | They propose a framework for multi-turn LLM agents that integrates global context . they propose GRPO, which integrates success-rate-aware modulation and proximity-based soft aggregation . |
| Outcome: | The proposed framework yields performance gains over existing baselines with negligible computational cost. |