Think Anywhere in Code Generation
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 imi...
| Authors | Xue Jiang, Tianyu Zhang, Ge Li et al. |
|---|---|
| Primary category | cs.SE |
| Published | 31 March 2026 |
| Last updated at source | 27 April 2026 |
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