Enhancing the Code Reasoning Capabilities of LLMs via Consistency-based Reinforcement Learning
Code reasoning refers to the task of predicting the output of a program given its source code and specific inputs. It can measure the reasoning capability of large language models (LLMs) and also benefit downstream tasks such as code generation and mathematical reasoning. Existing work has verified the effectiveness of reinforcement learning on the task. However, these methods design rewards solely based on final outputs or coarse-grained signals, and neglect the inherent consistency of the stepwise reasoning process in the task. Therefore, these methods often result in sparse reward or reward hacking, which limits the full play of enhanced learning capabilities. To alleviate these issues...
| Authors | Zhanyue Qin, Jia Feng, Yibo Lyu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 18 May 2026 |
| Last updated at source | 18 May 2026 |
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