Papers by Runxin Cai
LLM Critics Help Catch Bugs in Mathematics: Towards a Better Mathematical Verifier with Natural Language Feedback (2025.findings-acl)
Copied to clipboard
Bofei Gao, Zefan Cai, Runxin Xu, Peiyi Wang, Ce Zheng, Runji Lin, Keming Lu, Dayiheng Liu, Chang Zhou, Wen Xiao, Tianyu Liu, Baobao Chang
| Challenge: | Existing mathematical verifiers are trained with binary classification labels, which are not informative enough for the model to accurately assess the solutions. |
| Approach: | They propose a natural language feedback-enhanced verifier that can validate the correctness of response generated by policy models by constructing automatically generated training data and a two-stage training paradigm. |
| Outcome: | The proposed verifier significantly improves in verification and reinforcement learning and alleviates data-demanding problems of the reward model. |
EULoInf: Efficient Hessian-Free Entropy Based Uncertainty-Aware Data Influence Approximation (2026.findings-acl)
Copied to clipboard
| Challenge: | Extensive studies show that the effectiveness of fine-tuning heavily relies on the quality of training data. |
| Approach: | They propose a framework that approximates influence via uncertainty and gradient based validation loss lookahead. |
| Outcome: | The proposed framework matches or outperforms prior methods across diverse tasks and LLM architectures while reducing computational time and memory usage by over 50%. |