New Method Improves Small Language Model Reasoning Without Relying on Larger Models
Researchers have developed a technique called Select to Think (S2T) that helps small language models (SLMs) perform better at reasoning tasks by leveraging the fact that larger language models' preferred answers often appear in the SLM's top candidate predictions. The method reframes the role of larger models from generating text to selecting among the smaller model's proposals, which is simpler to teach through distillation. This approach allows small models to improve reasoning performance by 24% on math tasks while maintaining computational efficiency without needing a larger model at inference time.
Researchers at arXiv have identified a phenomenon called "local sufficiency" in small language models: when these models struggle with reasoning tasks, the preferred answer from a larger language model often still appears within the smaller model's top-K candidate predictions, even if it's not ranked first. Based on this insight, they propose Select to Think (S2T), which changes how large and small models interact during training. Instead of having the large model generate complete text for the small model to imitate, the large model simply ranks the small model's candidate answers. This simpler supervision signal is then distilled into the small model through S2T-Local, enabling it to perform autonomous re-ranking without needing the larger model during actual use. Empirical results show that a 1.5 billion parameter small model's top-8 predictions contain the preferred answer from a 32 billion parameter model 95% of the time, and the approach achieves a 24.1% relative improvement on math reasoning tasks while matching the performance of more computationally expensive methods.
What's missing
The study does not discuss potential limitations of the local sufficiency assumption across different domains or task types, nor does it address how performance scales with different model size ratios or whether the approach generalizes to non-reasoning tasks.
What different sources said
- arXiv cs.CLCenter
Select to Think: Unlocking SLM Potential with Local Sufficiency
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