Tandem Framework Combines Large and Small Language Models for Efficient Reasoning

A novel collaborative model known as Tandem, outlined in a paper on arXiv (2604.23623), suggests an integration of large language models (LLMs) with small language models (SLMs) to lower computational expenses while ensuring superior reasoning quality. In this framework, the LLM serves as a strategic guide, producing a concise set of essential reasoning insights. These insights direct a smaller, more efficient SLM to carry out the complete reasoning process and formulate the final answer. Tandem also features a cost-aware termination mechanism that intelligently determines when to conclude the LLM's role, optimizing both efficiency and dependability. This approach tackles the significant computational demands of reasoning-heavy inference methods that involve explicit step-by-step reasoning before arriving at conclusions.