Papers by Zifan He
Towards Hierarchical Multi-Step Reward Models for Enhanced Reasoning in Large Language Models (2026.findings-acl)
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Teng Wang, Jiang Zhangyi, Zhenqi He, Hailei Gong, Shenyang Tong, Wenhan Yang, Zeyu Li, Yanan Zheng, Zifan He, Zewen Ye, Shengjie Ma, Jianping Zhang
| Challenge: | Existing Process Reward Models (PRMs) are vulnerable to reward hacking and require expensive, large-scale annotation of reasoning steps. |
| Approach: | They propose a reward model approach which evaluates both individual and consecutive reasoning steps from fine-grained and coarse-grounded level. |
| Outcome: | Empirical results show that the proposed model performs better than existing PRMs and is more robust than existing models. |
HMT: Hierarchical Memory Transformer for Efficient Long Context Language Processing (2025.naacl-long)
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| Challenge: | Existing models that memorize past tokens have “flat” memory architectures that restrict the context window. |
| Approach: | They propose a framework that imitates human memorization behavior by preserving tokens from early input segments, passing memory embeddings along the sequence, and recalling relevant information from history. |
| Outcome: | The proposed framework outperforms existing models in language modeling and question-answering tasks and achieves comparable or superior generation quality to long-context models with 2 57 fewer parameters and 2.5 116 less inference memory. |