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 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.
69cc80c53f84a0971be66ae6
Xue Jiang
69cc80c53f84a0971be66ae7
Tianyu Zhang
69cc80c53f84a0971be66ae8
Ge Li
69cc80c53f84a0971be66ae9
Mengyang Liu
69cc80c53f84a0971be66aea
Taozhi Chen
69cc80c53f84a0971be66aeb
Zhenhua Xu
69cc80c53f84a0971be66aec
Binhua Li
69cc80c53f84a0971be66aed
Wenpin Jiao
69cc80c53f84a0971be66aee
Zhi Jin
69cc80c53f84a0971be66aef
Yongbin Li
69cc80c53f84a0971be66af0
Yihong Dong
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 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.
Think-Anywhere is a novel reasoning mechanism that enables large language models to invoke thinking on-demand during code generation, improving performance across multiple benchmarks through adaptive reasoning allocation.
Large Language Models code generation reasoning mechanisms on-demand thinking cold-start training outcome-based RL rewards autonomous exploration high-entropy positions interpretability