Thinking with Reasoning Skills: Fewer Tokens, More Accuracy
Reasoning LLMs often spend substantial tokens on long intermediate reasoning traces (e.g., chain-of-thought) when solving new problems. We propose to summarize and store reusable reasoning skills distilled from extensive deliberation and trial-and-error exploration, and to retrieve these skills at inference time to guide future reasoning. Unlike the prevailing \emph{reasoning from scratch} paradigm, our approach first recalls relevant skills for each query, helping the model avoid redundant detours and focus on effective solution paths. We evaluate our method on coding and mathematical reasoning tasks, and find that it significantly reduces reasoning tokens while improving overall perform...
| Authors | Guangxiang Zhao, Qilong Shi, Xusen Xiao et al. |
|---|---|
| Primary category | cs.AI |
| Published | 23 April 2026 |
| Last updated at source | 25 April 2026 |
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