New Method Improves Small Language Model Reasoning Without Relying on Larger Models
Researchers have proposed a technique called Select to Think (S2T) that significantly improves the reasoning ability of small language models (SLMs) by reframing a large language model's role as a selector among the SLM's own candidate outputs rather than a generator. The key insight, termed 'local sufficiency,' is that a large model's preferred next token is almost always already present within the small model's top-K predictions, even if not ranked first. Accepted to ICML 2026, the method achieves a 24.1% relative improvement in math reasoning for a 1.5B-parameter model while eliminating the need for a large model at inference time.
The paper introduces Select to Think (S2T), a framework designed to close the reasoning gap between small language models (SLMs) and large language models (LLMs) without the high latency and cost of invoking an LLM during inference. The central finding, called 'local sufficiency,' demonstrates that a 1.5B-parameter SLM's top-8 next-token candidates contain the preferred choice of a 32B LLM 95% of the time, even when the SLM fails to rank that token first. Rather than asking the LLM to generate text from scratch at reasoning divergence points, S2T repurposes the LLM to simply select among the SLM's own proposals, reducing the supervision signal to discrete candidate rankings. A distilled variant, S2T-Local, internalizes this selection logic into the SLM itself, enabling autonomous re-ranking at inference time with no LLM dependency. Empirically, S2T-Local improves the 1.5B SLM's average math performance by 24.1% relative to greedy decoding, matching the effectiveness of 8-path self-consistency while requiring only a single generation trajectory. The work was accepted to ICML 2026 and code has been made publicly available.
What's missing
The paper focuses on math reasoning benchmarks; generalization to other reasoning domains (e.g., coding, commonsense, scientific reasoning) is not fully characterized. It is also unclear how performance scales with different SLM or LLM size pairings beyond the 1.5B/32B combination tested, and the computational overhead of the distillation training phase is not discussed in the abstract.
What different sources said
- arXiv cs.CLCenter
Select to Think: Unlocking SLM Potential with Local Sufficiency
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