Papers by Ruopei Sun
CodeContests-O: Powering LLMs via Feedback-Driven Iterative Test Case Generation (2026.findings-acl)
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Jianfeng Cai, Jinhua Zhu, Ruopei Sun, Kangwen Zhao, Dongyun Xue, Mingxiao Feng, Wengang Zhou, Houqiang Li
| Challenge: | Existing approaches to synthesize test cases using Large Language Models (LLMs) rely on the model’s intrinsic generation capabilities without external feedback, resulting in insufficiently diverse cases. |
| Approach: | They propose a feedback-driven iterative framework that leverages Large Language Models to generate initial test cases, execute them against known correct and incorrect solutions, and utilizes the failed results as feedback to guide the LLM in refining the test cases toward high fidelity and discriminability. |
| Outcome: | The proposed method outperforms the existing codecontests and codecontests+ models by 4.30% and 8.78%. |
Bias Fitting to Mitigate Length Bias of Reward Model in RLHF (2026.acl-long)
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| Challenge: | Existing approaches to tackling length bias are limited by their complexity or lack of a linear length-reward relation. |
| Approach: | They propose a framework that learns and corrects underlying bias patterns by fitting a length-reward relationship into a reward model. |
| Outcome: | The proposed framework improves length-controlled win rate and reduces verbosity without compromising performance. |