Papers by Zhuoyue Chen
I²B-LPO: Latent Policy Optimization via Iterative Information Bottleneck (2026.acl-long)
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Huilin Deng, Hongchen Luo, Yue Zhu, Long Li, Zhuoyue Chen, Xinghao Zhao, Ming LI, Chuyang Zhao, Jihai Zhang, MengChang Wang, Yang Cao, Yu Kang
| Challenge: | Existing methods for large language model reasoning suffer from exploration collapse due to the semantic homogeneity of random rollouts. |
| Approach: | They propose to use latent policy optimization via iterative information bottleneck to optimize reasoning trajectories by diversifying reasoning . |
| Outcome: | Empirical results show that the proposed method achieves state-of-the-art performance with margins of up to 5.3% in accuracy and 7.4% in diversity metrics. |
Scale Down to Speed Up: Dynamic Data Selection for Reinforcement Learning (2025.findings-emnlp)
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| Challenge: | Current approaches to Reinforcement Learning (RL) rely on massive static datasets, leading to computational inefficiency and redundant gradient updates. |
| Approach: | They propose a data-centric RL framework that dynamically selects the most informative training samples to optimize RL for mathematical reasoning. |
| Outcome: | The proposed framework achieves comparable performance to full-data training methods while requiring only 1.5K samples instead of 220K, reducing training time from 13 days to just 4 hours on 8A800 GPUs. |