Papers by Zhengxu Hou
EvolvR: Self-Evolving Pairwise Reasoning for Story Evaluation to Enhance Generation (2026.acl-long)
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Xinda Wang, Zhengxu Hou, Yangshijie Zhang, null Yanbingren, Jialin Liu, ChenZhuo Zhao, Zhibo Yang, Bin-Bin Yang, Feng Xiao
| Challenge: | Existing methods for story evaluation lack reasoning capabilities for open-source models . evolvR framework provides high-fidelity evaluators for story generation tasks . |
| Approach: | They propose a framework that self-synthesizes chain-of-thought data via a multi-persona strategy . they propose evolvR to provide a reward model for story generation . |
| Outcome: | The proposed framework achieves state-of-the-art performance on three evaluation benchmarks . it also enhances the quality of generated stories, validating the superiority of the framework . |
Imperfect also Deserves Reward: Multi-Level and Sequential Reward Modeling for Better Dialog Management (2021.naacl-main)
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| Challenge: | Existing research on taskoriented dialog systems mainly includes pipeline and end-to-end methods due to its non-differentiable nature. |
| Approach: | They propose a multi-level reward modeling approach that factorizes a reward into a three-level hierarchy: domain, act, and slot. |
| Outcome: | The proposed approach significantly improves performance and speed of training in a wide range of dialog systems. |
Triviality Corrected Endogenous Reward (2026.acl-long)
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Xinda Wang, Zhengxu Hou, Yangshijie Zhang, null Yanbingren, Jialin Liu, ChenZhuo Zhao, Zhibo Yang, Bin-Bin Yang, Feng Xiao
| Challenge: | Recent work on unsupervised reinforcement learning for mathematical reasoning using confidence-based endogenous rewards focuses on open-ended text generation, requiring either annotated data or powerful closed-source models. |
| Approach: | They propose a method that rewards the relative information gain between a specialist and a generalist reference policy, modulated by a probability-dependent correction mechanism. |
| Outcome: | The proposed model improves on multiple writing benchmarks and model architectures without external supervision and validates generality across different generation tasks. |