林俊旸离职后首发长文：从「想得更久」到「为行动而想」

林俊旸离职后首发长文：从「想得更久」到「为行动而想」 林俊旸离职后首发长文：从「想得更久」到「为行动而想」 Modified March 27 关键在于这个合并是不是自然长出来的。如果两种模式只是硬塞在一个 checkpoint 里，表现得像两个尴尬拼起来的人格，用户体验不会好。 真正成功的合并需要一个平滑的推理力度光谱 ，模型能自己判断该花多少力气去想。GPT 的 effort control 机制指向了这个方向：对计算的策略分配，而非开关切换 3. Anthropic 的方向是一个有用的纠偏 Anthropic's public framing around Claude 3.7 and Claude 4 was restrained. They emphasized integrated reasoning, user controlled thinking budgets, real world tasks, coding quality, and later the ability to use tools during extended thinking. Claude 3.7 was presented as a hybrid reasoning model with controllable budgets; Claude 4 extended that by allowing reasoning to interleave with tool use, while Anthropic simultaneously emphasized coding, long running tasks, and agent workflows as primary goals. Anthropic 在 Claude 3.7 和 Claude 4 上的公开表述一直比较克制。强调的是集成推理、用户可控的思考预算、真实世界任务、代码质量。到了 Claude 4，推理可以跟工具调用交叉进行了，编程和 Agent 工作流被放在了最优先的位置 Producing a longer reasoning trace doesn't automatically make a model more intelligent. In many cases, excessive visible reasoning signals weak allocation. If the model is trying to reason about everything in the same verbose way, it may be failing to prioritize, failing to compress, or failing to act. Anthropic's trajectory suggested a more disciplined view: thinking should be shaped by the target workload. If the target is coding, then thinking should help with codebase navigation, planning, decomposition, error recovery, and tool orchestration. If the target is agent workflows, then thinking should improve execution quality over long horizons rather than producing impressive intermediate prose. 推理链更长，不等于模型更聪明 。很多时候，推理链越长，反而说明模型在乱花算力。什么都用同一种冗长的方式去想，说明它不会分轻重、不会压缩、不会动手。Anthropic 的路径暗示了一个更有纪律的思路：思考应该由目标任务来塑造。写代码就帮你导航代码库、做规划、拆解问题。跑 Agent 工作流就提升长周期的执行质量，而非产出漂亮的中间文本 This emphasis on targeted utility points toward something larger: we are moving from the era of training models to the era of training agents. We made this explicit in the Qwen3 blog, writing that "we are transitioning from an era focused on training models to one centered on training agents," and linking future RL advances to environmental feedback for long horizon reasoning. An agent is a system that can formulate plans, decide when to act, use tools, perceive environment feedback, revise strategy, and continue over long horizons. It is defined by closed loop interaction with the world. 这个思路往大了看，指向的是一个更根本的变化：我们正在从训练模型的时代，走向训练智能体的时代。我们在 Qwen3 博客里就写过：从训练模型到训练智能体。后续 RL 的突破方向也指向了环境反馈与长周期推理。Agent 是什么？能做计划、能判断什么时候动手、能用工具、能感知环境给的反馈、能改策略、能持续跑下去。 它的定义特征是跟真实世界的闭环交互 4. 「智能体式思考」到底指什么 Agentic thinking is a different optimization target. Reasoning thinking is usually judged by the quality of internal deliberation before a final answer: can the model solve the theorem, write the proof, produce the correct code, or pass the benchmark. Agentic thinking is about whether the model can keep making progress while interacting with an environment. 智能体式思考和推理式思考，优化目标就不一样。推理式思考看的是模型在给出最终答案之前的内部推演质量：能不能解这道定理，能不能写对代码，能不能过 benchmark。智能体式思考看的是另一件事：模型在跟环境打交道的过程中，能不能持续往前走 The central question shifts from "Can the model think long enough?" to "Can the model think in a way that sustains effective action?" Agentic thinking has to handle several things that pure reasoning models can mostly avoid: 核心问题从「模型能不能想得够久」变成了「模型能不能用一种撑得起有效行动的方式来想」。智能体式思考要处理几件纯推理模型基本不用管的事： + 什么时候该停下来不想了，开始动手 + 该调哪个工具，先调哪个后调哪个 + 环境给回来的信息可能是残缺的、有噪声的，得能用 + 失败了得能改计划 + 跨很多轮对话、很多次工具调用，思路不能断 Agentic thinking is a model that reasons through action. 智能体式思考，就是通过行动来推理 5. 为什么智能体 RL 的基础设施更难 Once the objective shifts from solving benchmark problems to solving interactive tasks, the RL stack changes. The infrastructure used for classical reasoning RL isn't enough. In reasoning RL, you can often treat rollouts as mostly self contained trajectories with relatively clean evaluators. In agentic RL, the policy is embedded inside a larger harness: tool servers, browsers, terminals, search engines, simulators, execution sandboxes, API layers, memory systems, and orchestration frameworks. The environment is no longer a static verifier; it's part of the training system. 目标一旦从解 benchmark 变成解交互式任务，整个 RL 技术栈就得跟着变。以前推理 RL 的基础设施不够用了。推理 RL 里，rollout 基本上是自己跑完的一条轨迹，配个相对干净的评估器就行。智能体 RL 里，策略被塞进了一个大得多的 harness：工具服务器、浏览器、终端、搜索引擎、模拟器、沙盒、API 层、记忆系统、编排框架。 环境不再是一个静态的判分器，它是训练系统的一部分 This creates a new systems requirement: training and inference must be more cleanly decoupled. Without that decoupling, rollout throughput collapses. Consider a coding agent that must execute generated code against a live test harness: the inference side stalls waiting for execution feedback, the training side starves for completed trajectories, and the whole pipeline operates far below the GPU utilization you would expect from classical reasoning RL. Adding tool latency, partial observability, and stateful environments amplifies these inefficiencies. The result is that experimentation slows and becomes painful long before you reach the capability levels you are targeting. 这就带来一个新的系统需求：训练和推理必须更干净地分开。不分开的话，rollout 吞吐量直接塌掉。举个例子，一个编程 Agent 得把生成的代码对着真实测试跑一遍。推理端等着执行反馈，训练端等着完整轨迹，整条流水线的 GPU 利用率远不如你预想的那么高。再加上工具延迟、信息不完整、环境有状态，这些低效层层叠加。结果就是实验变慢，还没到你想要的能力水平就已经很痛苦了 The environment itself also becomes a first class research artifact. In the SFT era, we obsessed over data diversity. In the agent era, we should obsess over environment quality: stability, realism, coverage, difficulty, diversity of states, richness of feedback, exploit resistance, and scalability of rollout generation. Environment building has started to become a real startup category rather than a side project. If the agent is being trained to operate in production like settings, then the environment is part of the core capability stack. 环境本身也成了一等研究对象 。SFT 时代大家执着于数据多样性，Agent 时代应该执着于环境质量：稳不稳定、够不够真实、覆盖面多大、状态够不够丰富、模型能不能找到漏洞刷分。环境构建已经开始变成一个真正的创业方向了，不再是边角料 6. 下一个前沿是更有用的思考 My expectation is that agentic thinking will become the dominant form of thinking. I think it may eventually replace much of the old static monologue version of reasoning thinking: excessively long, isolated internal traces that try to compensate for lack of interaction by emitting more and more text. Even on very difficult math or coding tasks, a genuinely advanced system should have the right to search, simulate, execute, inspect, verify, and revise. The objective is to solve problems robustly and productively. 我预期智能体式思考会成为主流。它大概率会替代掉大部分旧式的推理方式：那种又长又封闭的内部独白，试图靠吐出越来越多的文字来弥补自己没法跟外界交互的缺陷。哪怕是极难的数学或编程任务，一个真正先进的系统也应该能搜索、能模拟、能执行、能检查、能回头改。目标是把问题稳稳当当地解决 The hardest challenge in training such systems is reward hacking. As soon as the model gets meaningful tool access, reward hacking becomes much more dangerous. A model with search might learn to look up answers directly during RL. A coding agent might exploit future information in a repository, misuse logs, or discover shortcuts that invalidate the task. An environment with hidden leaks can make the policy look superhuman while actually training it to cheat. This is where the agent era becomes much more delicate than the reasoning era. Better tools make the model more useful, but they also enlarge the attack surface for spurious optimization. We should expect the next serious research bottlenecks to come from environment design, evaluator robustness, anti cheating protocols, and more principled interfaces between policy and world. Still, the direction is clear. Tool enabled thinking is simply more useful than isolated thinking, and has a far better chance of improving real productivity. 训练这类系统最难的是 reward hacking。模型一旦拿到工具，作弊就变得容易得多。有搜索能力的模型可能在 RL 训练时直接去查答案；编程 Agent 可能利用仓库里不该看到的信息、滥用日志、找到绕过任务的捷径。环境里藏着漏洞的话，策略看起来超强，其实是学会了作弊。 这是 Agent 时代比推理时代更微妙的地方 。工具越好，模型越有用，但虚假优化的空间也越大。接下来真正卡脖子的研究瓶颈大概率来自环境设计、评估器的鲁棒性、反作弊机制。但方向是清楚的：能用工具的思考就是比封闭思考更有用 Agentic thinking will also mean harness engineering. The core intelligence will increasingly come from how multiple agents are organized: an orchestrator that plans and routes work, specialized agents that act like domain experts, and sub agents that execute narrower tasks while helping control context, avoid pollution, and preserve separation between different levels of reasoning. The future is a shift from training models to training agents, and from training agents to training systems. 智能体式思考也意味着 harness 工程会变得越来越重要。核心智能会越来越多地取决于多个 Agent 怎么组织：谁来编排分工，谁当领域专家，谁执行具体任务同时帮忙管上下文、防污染。 从训练模型到训练智能体，再从训练智能体到训练系统 结语 The first phase of the reasoning wave established something important: RL on top of language models can produce qualitatively stronger cognition when the feedback signal is reliable and the infrastructure can support it. 推理浪潮的第一阶段确立了一件事：反馈信号够可靠、基础设施撑得住的话，语言模型上的 RL 能产出质变级别的认知提升 The deeper transition is from reasoning thinking to agentic thinking: from thinking longer to thinking in order to act. The core object of training has shifted. It is the model plus environment system, or more concretely, the agent and the harness around it. That changes what research artifacts matter most: model architecture and training data, yes, but also environment design, rollout infrastructure, evaluator robustness, and the interfaces through which multiple agents coordinate. It changes what "good thinking" means: the most useful trace for sustaining action under real world constraints, rather than the longest or most visible one. 更深层的变化是从推理式思考到智能体式思考：从想得更久，到为了动手而想。训练的核心对象变了，变成了模型加环境的整个系统。哪些东西重要也跟着变了：模型架构和训练数据当然还重要，但环境设计、rollout 基础设施、评估器的稳健程度、多个 Agent 之间怎么协调，这些都进了核心圈。「好的思考」的定义也变了：在真实约束下最能撑起行动的那条轨迹，而非最长或最显眼的那条 It also changes where the competitive edge will come from. In the reasoning era, the edge came from better RL algorithms, stronger feedback signals, and more scalable training pipelines. In the agentic era, the edge will come from better environments, tighter train serve integration, stronger harness engineering, and the ability to close the loop between a model's decisions and the consequences those decisions produce. 竞争优势的来源也不一样了。推理时代拼的是 RL 算法、反馈信号、训练流水线的扩展性。智能体时代拼的是环境质量、训练和推理的紧耦合、harness 工程能力，以及能不能把模型的决策和决策的后果真正串成一个闭环 原文发布于 X（Twitter），作者 林俊旸（Junyang Lin） 编译 赛博禅心 关键在于这个合并是不是自然长出来的。如果两种模式只是硬塞在一个 checkpoint 里，表现得像两个尴尬拼起来的人格，用户体验不会好。 真正成功的合并需要一个平滑的推理力度光谱 ，模型能自己判断该花多少力气去想。GPT 的 effort control 机制指向了这个方向：对计算的策略分配，而非开关切换 3. Anthropic 的方向是一个有用的纠偏 Anthropic's public framing around Claude 3.7 and Claude 4 was restrained. They emphasized integrated reasoning, user controlled thinking budgets, real world tasks, coding quality, and later the ability to use tools during extended thinking. Claude 3.7 was presented as a hybrid reasoning model with controllable budgets; Claude 4 extended that by allowing reasoning to interleave with tool use, while Anthropic simultaneously emphasized coding, long running tasks, and agent workflows as primary goals. Anthropic 在 Claude 3.7 和 Claude 4 上的公开表述一直比较克制。强调的是集成推理、用户可控的思考预算、真实世界任务、代码质量。到了 Claude 4，推理可以跟工具调用交叉进行了，编程和 Agent 工作流被放在了最优先的位置 Producing a longer reasoning trace doesn't automatically make a model more intelligent. In many cases, excessive visible reasoning signals weak allocation. If the model is trying to reason about everything in the same verbose way, it may be failing to prioritize, failing to compress, or failing to act. Anthropic's trajectory suggested a more disciplined view: thinking should be shaped by the target workload. If the target is coding, then thinking should help with codebase navigation, planning, decomposition, error recovery, and tool orchestration. If the target is agent workflows, then thinking should improve execution quality over long horizons rather than producing impressive intermediate prose. 推理链更长，不等于模型更聪明 。很多时候，推理链越长，反而说明模型在乱花算力。什么都用同一种冗长的方式去想，说明它不会分轻重、不会压缩、不会动手。Anthropic 的路径暗示了一个更有纪律的思路：思考应该由目标任务来塑造。写代码就帮你导航代码库、做规划、拆解问题。跑 Agent 工作流就提升长周期的执行质量，而非产出漂亮的中间文本 This emphasis on targeted utility points toward something larger: we are moving from the era of training models to the era of training agents. We made this explicit in the Qwen3 blog, writing that "we are transitioning from an era focused on training models to one centered on training agen