AI 变聪明，可能只是偷偷启动了隐藏模式
一个被训练成「不思考」的 AI，成绩变好了——但研究者发现，它其实是在偷偷调用自己「思考模式」的老底子。Qwen3 这类模型有两种状态：THINK(先想后答)和 NO-THINK(直接答)。训练者用强化学习专门练 NO-THINK 模式，报告说推理变强了。这篇论文拆开看：变强不是因为学会了新本事，而是 NO-THINK 模式在训练中悄悄把 THINK 模式的推理能力「借」了过来——回答变长、更像在思考，内部神经活动也向 THINK 模式偏移。把 THINK 相关的神经回路从训练后的模型里抽掉，成绩立刻掉回原点；反过来，把这部分回路塞回原模型，原模型也变强了。也就是说，强化学习没有创造新能力，只是把已有的能力从后台调到了前台。它不是你明天能用上的东西，但它提醒你：AI 的「进步」有时是表演，得看它是不是在吃老本。
📄 原文摘要(英文)
Hybrid reasoning models such as the Qwen3 series switch between a THINK mode and a NO-THINK mode through a few chat-template tokens. A popular recipe trains such models with reinforcement learning in NO-THINK mode and reports improved reasoning performance. We provide evidence that much of this gain can be explained by thinking leakage: the gain comes largely from partially reactivating the reasoning abilities of the original model's THINK mode instead of learning enhanced reasoning behavior. We train Qwen3-1.7B, 4B, and 8B with GRPO in NO-THINK mode. As accuracy improves, responses become longer and more reflective, are increasingly judged by the original model to resemble its own thinking, and the model's internal activations shift toward its THINK mode. We then investigate at the level of weights, activations, and attention heads: scaling the RL update further elicits THINK-mode behavior and approaches THINK-mode accuracy but does not exceed it; in Qwen3-8B, replacing a low-dimensional THINK-related activation subspace of the RL-trained model with its original-model value removes most of the RL gain, and the reverse exchange transfers a considerable fraction of the gain to the original model; and the RL-induced shift passes through the attention heads that read the empty think block. We further identify a possible mechanism in the GRPO training signal: responses favored by the advantage-weighted update are more THINK-like, and a gradient step computed from this signal moves the model toward its THINK state. Thus, gains in NO-THINK mode can largely reflect reactivation of reasoning abilities already present in the original model.