What is AdaptThink?

AdaptThink is a novel reinforcement learning framework that trains an LLM when to think deeply and when to respond immediately. The idea is that not every query requires a long chain-of-thought; simple questions can be answered directly (“NoThinking” mode) more efficiently. However, the model must recognize harder problems where reasoning steps (“Thinking” mode) are necessary. AdaptThink uses a special reward objective to encourage an LLM to adaptively select the optimal mode based on the problem’s difficulty. During training, the model is penalized for unnecessary reasoning on easy queries, while still being rewarded for accuracy. This results in a meta-cognitive LLM that can skip explicit reasoning for easy tasks (saving time) and engage its chain-of-thought only for hard tasks – ultimately improving efficiency without sacrificing performance. AdaptThink demonstrates that reasoning models can learn when to think versus when to answer directly, balancing quality and speed (paper).