小模型先答，大模型只在必要时深度推理，省算力又保准确。
Adaptive Reasoning Executor: A Collaborative Agent System for Efficient Reasoning
- 小模型初答，大模型仅在错误时介入验证并深入推理。
- 简单任务中大模型使用量减少超50%，准确率损失极小。
- 适合需要高效推理且兼顾精度的复杂任务场景。
大型语言模型（LLMs）显示，思维链提示和深度推理能显著提升复杂任务表现，多代理系统通过模型辩论进一步提高准确性。然而，对所有问题都采用深度推理计算成本过高。为此，我们提出一种融合小模型与大模型的互补代理系统：小模型先生成初步答案，由大模型验证；若正确则直接采纳，否则大模型执行深度推理。实验表明，在简单问题上，该方法使大模型计算开销降低超过50%，准确率损失可忽略，同时在复杂任务上保持稳健性能。
原文摘要 · Abstract (English)
Recent advances in Large Language Models (LLMs) demonstrate that chain-of-thought prompting and deep reasoning substantially enhance performance on complex tasks, and multi-agent systems can further improve accuracy by enabling model debates. However, applying deep reasoning to all problems is computationally expensive. To mitigate these costs, we propose a complementary agent system integrating small and large LLMs. The small LLM first generates an initial answer, which is then verified by the large LLM. If correct, the answer is adopted directly; otherwise, the large LLM performs in-depth reasoning. Experimental results show that, for simple problems, our approach reduces the computational cost of the large LLM by more than 50% with negligible accuracy loss, while consistently maintaining robust performance on complex tasks.
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