To Think or Not to Think: Allocating Reasoning Where It Helps
Reinforcement learning (RL) has proven effective in enhancing the reasoning performance of large language models (LLMs), particularly in complex mathematical and programming tasks. However, this capability comes with systematic \textit{length misallocation}, in which models devote excessive reasoning to simple questions while terminating prematurely on harder ones, degrading inference efficiency with negligible accuracy improvement. Many length-adaptive methods mitigate this issue by allocating token budgets according to question difficulty, under the implicit assumption that harder questions benefit monotonically from extended reasoning. In contrast, we find that the effect of reasoning ...
| Authors | Zhengdong He, Yunfan Zhou, Jianguo Yao et al. |
|---|---|
| Primary category | cs.AI |
| Published | 30 August 2026 |
| Last updated at source | 30 August 2026 |
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