Small language models (SLMs) offer computational efficiency for scalable deployment, yet they often fall short of the reasoning power exhibited by their larger counterparts (LLMs). To mitigate this gap, current approaches invoke an LLM to generate tokens at points of reasoning divergence, but these external calls introduce substantial latency and costs. Alternatively, standard distillation is often hindered by the capacity limitation, as SLMs struggle 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 consistently 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 generation to selection among the SLM's proposals, simplifying the supervision signal to discrete candidate rankings. Leveraging this, we introduce S2T-LOCAL, which distills the selection logic into the SLM, empowering it to perform autonomous re-ranking without inference-time LLM dependency. Empirically, we demonstrate that a 1.5B SLM's top-8 candidates capture the 32B LLM's choice with 95% hit rate. Translating this potential into performance, S2T-LOCAL improves greedy decoding by 24.1% on average across benchmarks, effectively matching the efficacy of 8-path self-consistency while operating with single-trajectory efficiency.

翻译：小语言模型（SLM）凭借其计算效率优势适用于可扩展部署场景，但在推理能力上往往逊色于大型语言模型（LLM）。为弥合这一差距，现有方法通常在推理分歧点调用LLM生成标记，然而这种外部调用会引入显著的延迟和成本。另一方面，标准知识蒸馏常受限于SLM的能力瓶颈——面对LLM复杂的生成分布，小模型难以精准模仿。本研究通过识别“局部充分性”破解此困境：在分歧点处，LLM偏好的标记始终存在于SLM的Top-K次预测候选集中，尽管可能未成为SLM的Top-1选择。据此提出SELECT TO THINK（S2T）方法，将LLM的角色从开放式生成重构为对SLM候选序列的选择，将监督信号简化为离散候选排序。在此基础上推出S2T-LOCAL，将选择逻辑蒸馏至SLM自身，使其能在推理时无需依赖LLM独立完成重排序。实验表明：1.5B参数SLM的Top-8候选集能以95%命中率覆盖32B参数LLM的选择。将此潜力转化为性能提升后，S2T-LOCAL在基准测试中平均提升贪心解码方法24.1%的准确率，在保持单轨迹推理效率的同时，有效媲美8路径自洽性方法的效果。