Select to Think: Unlocking SLM Potential with Local Sufficiency
Small language models (SLMs) offer efficient deployment, yet they often lag behind their larger counterparts (LLMs) in reasoning. Existing remedies either invoke an LLM at points of reasoning divergence, incurring substantial latency and cost, or rely on standard distillation, which is limited by the SLM's capacity to accurately mimic the LLM's complex generative distribution. We address this dilemma by identifying local sufficiency: at divergence points, the LLM's preferred token often resides within the SLM's top-K next-token predictions, even when failing to emerge as the SLM top-1 choice. We therefore propose Select to Think (S2T), which reframes the LLM's role from open-ended generat...
| Authors | Wenxuan Ye, Yangyang Zhang, Xueli An et al. |
|---|---|
| Primary category | cs.CL |
| Published | 29 April 2026 |
| Last updated at source | 11 June 2026 |
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