LogitsCoder: Towards Efficient Chain-of-Thought Path Search via Logits Preference Decoding for Code Generation
arXiv:2602.14054v1 Announce Type: new Abstract: Code generation remains a challenging task that requires precise and structured reasoning. Existing Test Time Scaling (TTS) methods, including structured tree search, have made progress in exploring reasoning paths but still face two major challenges: (1) underthinking, where reasoning chains tend to be shallow and fail to capture the full complexity of problems; and (2) overthinking, where overly verbose reasoning leads to inefficiency and increased computational costs. To address these issues, we propose LogitsCoder, a novel framework that enhances chain-of-thought reasoning through lightweight, logit-level control mechanisms for code generation. LogitsCoder iteratively generates and refines reasoning steps by first steering token selection toward statistically preferred patterns via Logits Preference Decoding, then selecting and aggregating diverse reasoning paths using Logits Rank Based Path Selection and Thoughts Aggregation. This r
arXiv:2602.14054v1 Announce Type: new Abstract: Code generation remains a challenging task that requires precise and structured reasoning. Existing Test Time Scaling (TTS) methods, including structured tree search, have made progress in exploring reasoning paths but still face two major challenges: (1) underthinking, where reasoning chains tend to be shallow and fail to capture the full complexity of problems; and (2) overthinking, where overly verbose reasoning leads to inefficiency and increased computational costs. To address these issues, we propose LogitsCoder, a novel framework that enhances chain-of-thought reasoning through lightweight, logit-level control mechanisms for code generation. LogitsCoder iteratively generates and refines reasoning steps by first steering token selection toward statistically preferred patterns via Logits Preference Decoding, then selecting and aggregating diverse reasoning paths using Logits Rank Based Path Selection and Thoughts Aggregation. This results in coherent and effective reasoning chains that balance depth and efficiency. Extensive experiments demonstrate that LogitsCoder produces more efficient and higher-quality reasoning chains, leading to superior code generation performance compared to baseline methods.
Executive Summary
The article 'LogitsCoder: Towards Efficient Chain-of-Thought Path Search via Logits Preference Decoding for Code Generation' introduces a novel framework designed to enhance the efficiency and effectiveness of code generation through improved chain-of-thought reasoning. The authors address two critical challenges in existing Test Time Scaling (TTS) methods: underthinking, where reasoning chains are too shallow, and overthinking, which leads to verbose and computationally expensive reasoning. LogitsCoder employs Logits Preference Decoding to steer token selection towards preferred patterns and uses Logits Rank Based Path Selection and Thoughts Aggregation to refine and aggregate diverse reasoning paths. The framework aims to balance depth and efficiency, resulting in higher-quality reasoning chains and superior code generation performance. Extensive experiments validate the effectiveness of LogitsCoder compared to baseline methods.
Key Points
- ▸ LogitsCoder addresses the challenges of underthinking and overthinking in code generation.
- ▸ The framework uses Logits Preference Decoding to guide token selection.
- ▸ Logits Rank Based Path Selection and Thoughts Aggregation refine and aggregate reasoning paths.
- ▸ Experiments demonstrate improved efficiency and quality in code generation.
Merits
Innovative Approach
LogitsCoder introduces a novel method for enhancing chain-of-thought reasoning in code generation, addressing critical challenges in existing methods.
Balanced Reasoning
The framework effectively balances depth and efficiency in reasoning chains, leading to higher-quality code generation.
Empirical Validation
Extensive experiments demonstrate the superior performance of LogitsCoder compared to baseline methods.
Demerits
Complexity
The framework's reliance on logit-level control mechanisms may introduce complexity in implementation and deployment.
Generalizability
The effectiveness of LogitsCoder may be limited to specific types of code generation tasks and may require further validation across diverse scenarios.
Expert Commentary
The article presents a significant advancement in the field of code generation by addressing the critical issues of underthinking and overthinking through a novel framework. LogitsCoder's innovative use of logit-level control mechanisms and path selection strategies offers a promising solution to the challenges faced by existing TTS methods. The empirical validation of the framework's effectiveness is particularly noteworthy, as it provides strong evidence of its superiority over baseline methods. However, the complexity introduced by the framework's mechanisms may pose challenges in practical implementation, and further research is needed to assess its generalizability across diverse coding tasks. The implications of LogitsCoder extend beyond technical advancements, potentially influencing policy decisions and ethical considerations in the use of AI for software development. Overall, the article makes a valuable contribution to the field and sets a strong foundation for future research in efficient and effective code generation.
Recommendations
- ✓ Further research should explore the generalizability of LogitsCoder across different types of code generation tasks and programming languages.
- ✓ Practical implementations of LogitsCoder should be evaluated in real-world scenarios to assess its impact on software development workflows and productivity.