Tandem: Riding Together with Large and Small Language Models for Efficient Reasoning
Recent advancements in large language models (LLMs) have catalyzed the rise of reasoning-intensive inference paradigms, where models perform explicit step-by-step reasoning before generating final answers. While such approaches improve answer quality and interpretability, they incur substantial computational overhead due to the prolonged generation sequences. In this paper, we propose Tandem, a novel collaborative framework that synergizes large and small language models (LLMs and SLMs) to achieve high-quality reasoning with significantly reduced computational cost. Specifically, the LLM serves as a strategic coordinator, efficiently generating a compact set of critical reasoning insights...
| Authors | Zichuan Fu, Xian Wu, Guojing Li et al. |
|---|---|
| Primary category | cs.AI |
| Published | 26 April 2026 |
| Last updated at source | 26 April 2026 |
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