AdaCoT: Pareto-Optimal Adaptive Chain-of-Thought Triggering via Reinforcement Learning
Chenwei Lou, Zewei Sun, Xinnian Liang, Meng Qu, Wei Shen, Wenqi Wang, Yuntao Li, Qingping Yang, Shuangzhi Wu
2025-05-20
Summary
This paper talks about AdaCoT, a new system that helps AI models decide when they actually need to use detailed step-by-step reasoning, so they don't waste computer power on easy problems.
What's the problem?
The problem is that large language models often use a lot of detailed reasoning for every question, even when it's not needed, which makes them slower and more expensive to run.
What's the solution?
To solve this, the researchers built a framework that uses reinforcement learning to teach the AI when to use step-by-step thinking and when to skip it, so it only spends extra effort on the hard questions.
Why it matters?
This matters because it makes AI models much more efficient, saving time and energy while still doing a great job on tough problems, which is really helpful as AI gets used more in everyday life.
Abstract
AdaCoT, an adaptive reasoning framework using reinforcement learning, reduces unnecessary Chain-of-Thought generation by LLMs, cutting computational costs without sacrificing performance on complex tasks.