Papers by Zhaoyang Chu
ExecVerify: White-Box RL with Verifiable Stepwise Rewards for Code Execution Reasoning (2026.acl-long)
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| Challenge: | Existing methods for code execution reasoning are limited by the difficulty of the training data. |
| Approach: | They propose a model that uses reinforcement learning to reward correct answers from execution traces. |
| Outcome: | The proposed model improves pass@1 by up to 5.9% on code generation tasks over strong baselines. |
TESTEVAL: Benchmarking Large Language Models for Test Case Generation (2025.findings-naacl)
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Wenhan Wang, Chenyuan Yang, Zhijie Wang, Yuheng Huang, Zhaoyang Chu, Da Song, Lingming Zhang, An Ran Chen, Lei Ma
| Challenge: | Existing methods to generate test cases using large language models are limited in their ability to generate unit test cases. |
| Approach: | They propose a test case generation benchmark that uses large language models to generate unit test cases. |
| Outcome: | The proposed test case generation benchmarks compare LLMs with commercial and open-source LLM platforms and find that they lack the ability to comprehend program logic and execution paths. |
Wait, We Don’t Need to “Wait”! Removing Thinking Tokens Improves Reasoning Efficiency (2025.findings-emnlp)
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| Challenge: | Recent advances in large reasoning models often introduce significant overthinking . this leads to verbose and redundant outputs that hinder efficiency. |
| Approach: | They propose a plug-and-play solution that disables explicit self-reflection . it suppresses tokens such as "Wait" and "Hmm" during inference . |
| Outcome: | The proposed approach reduces chain-of-thought trajectory length by up to 27%–51% in five R1-style model series without compromising model utility. |
CGBridge: Bridging Code Graphs and Large Language Models for Better Structure-Aware Code Understanding (2026.findings-acl)
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| Challenge: | Existing structure-aware approaches treat structure as serialized text prompts or auxiliary training objectives, failing to provide explicit guidance during inference. |
| Approach: | They propose a plug-and-play method that enhances Large Language Models with Code Graph information through an external, trainable Bridge module. |
| Outcome: | The proposed method decouples structural reasoning from textual generation without updating the backbone. |