LightReasoner: Can Small Language Models Teach Large Language Models Reasoning?

Large language models (LLMs) have demonstrated remarkable progress in reasoning, often through supervised fine-tuning (SFT).However, SFT is resource-intensive, relying on large curated datasets, rejection-sampled demonstrations, and uniform optimization across all tokens-even though only a fraction carry meaningful learning value.In this work, we explore a counterintuitive idea: can smaller language models (SLMs) teach larger language models (LLMs) by revealing high-value reasoning moments that reflect the latter's unique strength?We propose LightReasoner 1 , a novel framework that leverages the behavioral divergence between a stronger expert model (LLM) and a weaker amateur model (SLM).LightReasoner operates in two stages: (1) a sampling stage that pinpoints critical reasoning moments and constructs supervision examples capturing the expert's advantage through expert-amateur contrast, and (2) a fine-tuning stage that aligns the expert model with these distilled examples, amplifying its reasoning strengths.Across seven benchmarks, LightReasoner improves accuracy by up to 28.1%, while reducing time consumption by 90%, sampled problems by 80%, and tuned token usage by 99%, all without relying on ground-truth labels.By turning weaker SLMs into effective teaching signals, LightReasoner offers a scalable and resource-efficient approach for advancing LLM reasoning.