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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.29957 |
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| _version_ | 1866908994837151744 |
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| author | Jiang, Xue Zhang, Tianyu Li, Ge Liu, Mengyang Chen, Taozhi Xu, Zhenhua Li, Binhua Jiao, Wenpin Jin, Zhi Li, Yongbin Dong, Yihong |
| author_facet | Jiang, Xue Zhang, Tianyu Li, Ge Liu, Mengyang Chen, Taozhi Xu, Zhenhua Li, Binhua Jiao, Wenpin Jin, Zhi Li, Yongbin Dong, Yihong |
| contents | Recent advances in reasoning Large Language Models (LLMs) have primarily relied on upfront thinking, where reasoning occurs before final answer. However, this approach suffers from critical limitations in code generation, where upfront thinking is often insufficient as problems' full complexity only reveals itself during code implementation. Moreover, it cannot adaptively allocate reasoning effort throughout the code generation process where difficulty varies significantly. In this paper, we propose Think-Anywhere, a novel reasoning mechanism that enables LLMs to invoke thinking on-demand at any token position during code generation. We achieve Think-Anywhere by first teaching LLMs to imitate the reasoning patterns through cold-start training, then leveraging outcome-based RL rewards to drive the model's autonomous exploration of when and where to invoke reasoning. Extensive experiments on four mainstream code generation benchmarks (i.e., LeetCode, LiveCodeBench, HumanEval, and MBPP) show that Think-Anywhere achieves state-of-the-art performance over both existing reasoning methods and recent post-training approaches, while demonstrating consistent generalization across diverse LLMs. Our analysis further reveals that Think-Anywhere enables the model to adaptively invoke reasoning at high-entropy positions, providing enhanced interpretability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_29957 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Think Anywhere in Code Generation Jiang, Xue Zhang, Tianyu Li, Ge Liu, Mengyang Chen, Taozhi Xu, Zhenhua Li, Binhua Jiao, Wenpin Jin, Zhi Li, Yongbin Dong, Yihong Software Engineering Machine Learning Recent advances in reasoning Large Language Models (LLMs) have primarily relied on upfront thinking, where reasoning occurs before final answer. However, this approach suffers from critical limitations in code generation, where upfront thinking is often insufficient as problems' full complexity only reveals itself during code implementation. Moreover, it cannot adaptively allocate reasoning effort throughout the code generation process where difficulty varies significantly. In this paper, we propose Think-Anywhere, a novel reasoning mechanism that enables LLMs to invoke thinking on-demand at any token position during code generation. We achieve Think-Anywhere by first teaching LLMs to imitate the reasoning patterns through cold-start training, then leveraging outcome-based RL rewards to drive the model's autonomous exploration of when and where to invoke reasoning. Extensive experiments on four mainstream code generation benchmarks (i.e., LeetCode, LiveCodeBench, HumanEval, and MBPP) show that Think-Anywhere achieves state-of-the-art performance over both existing reasoning methods and recent post-training approaches, while demonstrating consistent generalization across diverse LLMs. Our analysis further reveals that Think-Anywhere enables the model to adaptively invoke reasoning at high-entropy positions, providing enhanced interpretability. |
| title | Think Anywhere in Code Generation |
| topic | Software Engineering Machine Learning |
| url | https://arxiv.org/abs/2603.29957 |