3月26日，被誉为"阿里最年青P10"的千问（Qwen）大模子灵魂东谈主物林俊旸，在月初下野风云公论渐息之际，在X平台发布长文《从"推理式念念考"到"智能步地念念考"》，系统弘扬了他对AI技能范式演进剖析。通过这篇著述，林俊旸不仅总结了畴昔，更明晰地指向了AI将来竞争的果真战场——一个卓绝单一模子比拼、关乎系统、环境与协同的智能体新期间。
著述明晰地勾画出一条AI智商进化的门路图。林俊旸将2024-2025年界说为"推理念念考"阶段，以OpenAI o1和DeepSeek-R1为代表，其中枢树立是阐明了"念念考"不错动作一种可查验、可请托的一流智商。这一阶段的骨子，是通过强化学习（RL）在数学、代码等可考据界限取得笃定性反应，从而让模子"为正确而优化，而非为合理"。关联词，这背后是巨大的基础关节挑战——推理RL已从轻量级微调附件，演变为需要大界限部署、高浑沌考据的系统工程问题。
不外，果真的难题远不啻于此。著述第二部分深刻探讨了"念念考模式"与"指示模式"会通的实践窘境。这一分析也照射了买卖现实：阿里在Qwen3尝试会通明，后续的2507版块中Instruct与Thinking版块零丁呈现，因为大宗客户在批量操作中仍需要高性价比、高可控的指示举止。
著述明确提倡"智能步地念念考"（Agentic Thinking）是下一代AI的中枢范式。这标志着查验中枢从模子自己转向 "模子-环境"系统。智能体念念维的中枢是"为行动而念念考"，它必须处理纯推理模子无需靠近的难题：决定何时行动、调用何种器具、处理环境的不笃定反应、在失败后纠正谈判、在多轮交互中保握连贯。
林俊旸以为，在推理期间，上风源于更好的RL算法和反应信号；而在智能体期间，竞争上风将建立在更优质的环境设想、更紧密的查验-劳动一体化架构、以及更矫健的智能体协同工程之上。环境自己成为一等品，其踏实性、真实性、反应丰富度和抗过拟合智商至关进攻。同期，多智能体组织架构——由策画者、界限众人和扩充子代理组成的系统——将成为中枢智能的开端。
这篇著述不错看作念是林俊旸对于技能理念的完好弘扬，将他任职期间推进Qwen发展的技能玄学系统化输出。粗略，这亦然一份个东谈主将来的宣言，著述中对"智能体期间"基础关节、环境工程进攻性的强调，默示了他看好的下一个创业或究诘标的。
全文由千问Qwen翻译：
From "Reasoning" Thinking to "Agentic" Thinking
从"推理式念念考"到"智能步地念念考"
The last two years reshaped how we evaluate models and what we expect from them. OpenAI's o1 showed that "thinking" could be a first-class capability， something you train for and expose to users. DeepSeek-R1 proved that reasoning-style post-training could be reproduced and scaled outside the original labs. OpenAI described o1 as a model trained with reinforcement learning to "think before it answers." DeepSeek positioned R1 as an open reasoning model competitive with o1.
畴昔两年重塑了咱们评估模子的形式以及对模子的盼愿。OpenAI的o1阐明，"念念考"不错成为一种一流的手段——一种需要专门查验并面向用户绽放的智商。DeepSeek-R1则标明，推理格调的后查验方法不仅能在原始实验室以外重现，还能已毕界限化应用。OpenAI将o1刻画为一种通过强化学习查验而成的模子，它能够在回答问题前"先进行念念考"。DeepSeek则将R1定位为一款与o1相比好意思的绽放式推理模子。
That phase mattered. But the first half of 2025 was mostly about reasoning thinking: how to make models spend more inference-time compute， how to train them with stronger rewards， how to expose or control that extra reasoning effort. The question now is what comes next. I believe the answer is agentic thinking: thinking in order to act， while interacting with an environment， and continuously updating plans based on feedback from the world.
阿谁阶段很进攻。但2025年上半年主要聚焦于推理念念维：怎样让模子在推理时消费更多时刻。运筹帷幄，怎样用更横蛮的奖励来查验它们，怎样表现或截止那种格外的推理奋力。刻下的问题是：接下来该奈何作念？我以为谜底是代理念念维：即念念考——为了 在与环境互动时选定行动，并字据来自外界的反应不休更新谈判。
1. What the Rise of o1 and R1 Actually Taught Us
o1和R1的崛起本质上教导了咱们什么
The first wave of reasoning models taught us that if we want to scale reinforcement learning in language models， we need feedback signals that are deterministic， stable， and scalable. Math， code， logic， and other verifiable domains became central because rewards in these settings are much stronger than generic preference supervision. They let RL optimize for correctness rather than plausibility. Infrastructure became critical.
第一波推理模子告诉咱们，若想在谈话模子中界限化应用强化学习，咱们就需要具备笃定性、踏实性和可蔓延性的反应信号。数学、代码、逻辑过甚他可考据的界限因此成为中枢，因为在这些场景中，奖励信号远比一般的偏好监督更为有劲。它们使强化学习能够专注于追求正确性，而非只是追求合感性。与此同期，基础关节也变得至关进攻。
Once a model is trained to reason through longer trajectories， RL stops being a lightweight add-on to supervised fine-tuning. It becomes a systems problem. You need rollouts at scale， high-throughput verification， stable policy updates， efficient sampling. The emergence of reasoning models was as much an infra story as a modeling story. OpenAI described o1 as a reasoning line trained with RL， and DeepSeek R1 later reinforced that direction by showing how much dedicated algorithmic and infrastructure work reasoning-based RL demands. The first big transition: from scaling pretraining to scaling post-training for reasoning.
一朝模子经过查验能够推理更长的轨迹，强化学习便不再只是监督微调的一个轻量级附加组件。它……变成 一个系统性问题。你需要大界限部署、高浑沌量考据、踏实的计谋更新以及高效的采样。推理模子的出现，其背后既触及基础关节开垦，也关乎建摹自己。OpenAI 将 o1 刻画为一种通过强化学习查验的推理模子，而 DeepSeek R1 其后进一步印证了这一标的，展示了——些许 针对基于推理的强化学习，需要专门的算法和基础关节责任。第一次紧要转机：从扩大预查验界限转向扩大后查验界限以已毕推明智商。
2. The Real Problem Was Never Just "Merge Thinking and Instruct"
果真的问题从来不单是是"会通念念考与指示"。
At the beginning of 2025， many of us in Qwen team had an ambitious picture in mind. The ideal system would unify thinking and instruct modes. It would support adjustable reasoning effort， similar in spirit to low / medium / high reasoning settings. Better still， it would automatically infer the appropriate amount of reasoning from the prompt and context， so the model could decide when to answer immediately， when to think longer， and when to spend much more computation on a truly difficult problem.
2025年头，咱们Qwen团队的很多成员心中齐刻画了一幅嘻是图的蓝图。逸想的系统是将已毕念念维与指示模式调解，并支握可调度的推理力度，其理念雷同于低/中/高三种推理设立。更棒的是，该系统能够字据教唆和高下文自动推断出妥当的推理量：模子既能即时作答，也能聘用深刻念念考，致使在靠近果真难办的问题时，插足更多运筹帷幄资源进行紧密求解。
Conceptually， this was the right direction. Qwen3 was one of the clearest public attempts. It introduced "hybrid thinking modes，" supported both thinking and non-thinking behavior in one family， emphasized controllable thinking budgets， and described a four-stage post-training pipeline that explicitly included "thinking mode fusion" after long-CoT cold start and reasoning RL.
从见地上讲，这是正确的标的。Qwen3是最明晰的公开尝试之一。它引入了"羼杂念念考模式"，在一个模子家眷中同期支握念念考和非念念考举止，强调可控的念念考预算，并刻画了一个明确包含"念念考模式会通"的四阶段后查验经过，该经过位于长念念维链冷启动和推理强化学习之后。
But merging is much easier to describe than to execute well. The hard part is data. When people talk about merging thinking and instruct， they often think first about model-side compatibility: can one checkpoint support both modes， can one chat template switch between them， can one serving stack expose the right toggles. The deeper issue is that the data distributions and behavioral objectives of the two modes are substantially different.
但会通比邃密扩充更容易刻画。勤奋的部分是数据。当东谈主们评论会通念念考与指示时，他们通常源流意想的是模子侧的兼容性：一个放哨点能否同期支握两种模式，一个聊天模板能否在它们之间切换，一个劳动栈能否表现正确的切换开关。更深层的问题是，这两种模式的数据散播和举止谈判存在骨子相反。
We did not get everything right when trying to balance model merging with improving the quality and diversity of post-training data. During that revision process， we also paid close attention to how users were actually engaging with thinking and instruct modes. A strong instruct model is typically rewarded for directness， brevity， formatting compliance， low latency on repetitive， high-volume enterprise tasks such as rewriting， labeling， templated support， structured extraction， and operational QA. A strong thinking model is rewarded for spending more tokens on difficult problems， maintaining coherent intermediate structure， exploring alternative paths， and preserving enough internal computation to meaningfully improve final correctness.
咱们在尝试均衡模子合并与训导查验后数据的质地和种种性时，并未绝对作念到白玉无瑕。在这一纠正过程中，咱们还密切热心了用户怎样本质参与具备念念考与相通两种模式。在企业级任务中，举例重写、标注、模板化支握、结构化索求以及运营质地保证等叠加性高、责任量大的场景，阐述强劲的相通模子通常因其平直性、简略性、样式合规性以及低延长而受到怜爱。而阐述强劲的念念考模子则因在管制难题时消耗更多标记、保握连贯的中间结构、探索多种备选旅途，并保留饱和的里面运筹帷幄以切实训导最终闭幕的正确性而备受崇尚。
These two behavior profiles pull against each other. If the merged data is not carefully curated， the result is usually mediocre in both directions: the "thinking" behavior becomes noisy， bloated， or insufficiently decisive， while the "instruct" behavior becomes less crisp， less reliable， and more expensive than what commercial users actually want.
这两种举止模式互相对消。要是对合并后的数据不加以经心筛选，最终闭幕通常两端不攀附：所谓的"念念考"型举止变得散洒落落、痴肥不胜，或穷乏饱和的决断力；而"指示"型举止则变得不够干脆利落、可靠性裁汰，且老本高于买卖用户的需求。本质上想要。
Separation remained attractive in practice. Later in 2025， after the initial hybrid framing of Qwen3， the 2507 line shipped distinct Instruct and Thinking updates， including separate 30B and 235B variants. In commercial deployment， a large number of customers still wanted high-throughput， low-cost， highly steerable instruct behavior for batch operations. For those scenarios， merging wasn't obviously a benefit. Separating the lines allowed teams to focus on solving the data and training problems of each mode more cleanly.
分离在实践中仍颇具劝诱力。2025年晚些时候，在Qwen3当先的羼杂框架之后，2507版块推出了零丁的Instruct和Thinking更新版块，其中包括划分针对30B和235B参数目的变体。在买卖部署中，大宗客户仍然但愿在批量操作中已毕高浑沌、低老本且高度可操控的指示举止。对于这些场景，合并彰着并不具备上风。将各条线分开，能让团队更明晰地专注于管制每种模式的数据和查验问题。
Other labs chose the opposite route. Anthropic publicly argued for an integrated model philosophy: Claude 3.7 Sonnet was introduced as a hybrid reasoning model where users could choose ordinary responses or extended thinking， and API users could set a thinking budget. Anthropic explicitly said they believed reasoning should be an integrated capability rather than a separate model. GLM-4.5 also publicly positioned itself as a hybrid reasoning model with both thinking and non-thinking modes， unifying reasoning， coding， and agent capabilities; DeepSeek later moved in a similar direction with V3.1's "Think & Non-Think" hybrid inference.
其他实验室则聘用了截然有异的旅途。Anthropic公开倡导一种集成式模子理念：Claude 3.7 Sonnet被定位为一种羼杂推理模子，用户可聘用往常陈述或深度念念考模式，API用户还可设定念念考预算。Anthropic明确透露，他们以为推理当当是一种集成化的智商，B体育官方网站首页而非零丁的模子。GLM-4.5通常公开将自身定位为一种羼杂推理模子，兼具念念考与非念念考两种模式，已毕了推理、编码及智能体智商的调解；DeepSeek随后也朝着雷同标的迈进，其V3.1版块推出了"念念考与非念念考"羼杂推理功能。
The key question is whether the merge is organic. If thinking and instruct are merely co-located inside one checkpoint but still behave like two awkwardly stitched personalities， the product experience remains unnatural. A truly successful merge requires a smooth spectrum of reasoning effort. The model should be able to express multiple levels of effort， and ideally choose among them adaptively. GPT-style effort control points toward this: a policy over compute， rather than a binary switch.
枢纽问题在于，这种会通是否是当然有机的。要是念念维与指示只是被安置于团结个放哨点内，却仍阐述为两种生硬拼接的个性，那么家具的用户体验将依然显得不当然。果真见效的会通，需要已毕推理奋力的平滑连系变化。模子应当能够抒发不同端倪的推理强度，而且最佳能自合适地在这些端倪之间作念出聘用。GPT式的奋力截止正朝着这一标的迈进：它选定的是对运筹帷幄资源的计谋性调控，而非陋劣的二元开关。
3. Why Anthropic's Direction Was a Useful Corrective
为什么Anthropic的主义是一种故意的纠正次序
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围绕Claude 3.7和Claude 4的公开表述是克制的。他们着重强调了整合推理、用户可控的念念维预算、真实宇宙任务、代码质地，以及后期在永劫刻念念考过程中使用器具的智商。Claude 3.7被定位为一种具备可控预算的羼杂推理模子；Claude 4则在此基础上进一步拓展，允许推理与器具使用互相交汇。与此同期，Anthropic还特等强调了编码、永恒运行任务以及智能体责任流动作其主要谈判。
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.
生成更长的推理轨迹并不会自动使模子变得更智能。在许厚情况下，过多的显式推理信号反而会导致分派遵守低下。要是模子试图以通常冗长的形式对扫数内容进行推理，它很可能无法合理 prioritization，无法有用压缩，也无法选定行动。东谈主类的 轨迹标明，一种更严谨的视角更为妥当：念念考应以谈判责任量为导向。要是谈判是编写代码，那么念念考就应有助于代码库导航、策画、剖析、造作规复以及器具编排。要是谈判是代理责任流，那么念念考的要点应放在训导永恒扩充质地上，而非追求令东谈主惊艳的中间后果。
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.
这种对谈判导向型实用性的强调，指向了一个更为重大的趋势：咱们正从模子查验期间迈向智能体查验期间。咱们在Qwen3博客中明确指出："咱们正在从一个以模子查验为中枢的期间，转型为以智能体查验为中枢的期间"，并把将来的强化学习进展与环境反应相集中，以支握永劫程的推明智商。所谓智能体，是一种能够制定谈判、决定行动时机、专揽器具、感知环境反应、调节计谋，并在长周期内握续运行的系统。它之是以如胶如漆，就在于其与外界之间造成了闭环互动。
4. What "Agentic Thinking" Really Means
"智能步地念念考"的果真含义
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.
"智能步地念念考"是一种不同的优化谈判。推理念念维通常以最终谜底之前的里面筹议质地来揣摸：模子能否解出定理、写出阐明、生成正确的代码，或通过基准测试。而"智能步地念念考"则热心的是，模子在与环境交互的过程中能否握续取得进展。
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:
Deciding when to stop thinking and take an action
Choosing which tool to invoke and in what order
Incorporating noisy or partial observations from the environment
Revising plans after failures
Maintaining coherence across many turns and many tool calls
Agentic thinking is a model that reasons through action.
中枢问题从"模子能否念念考饱和长的时刻？"转机为"模子能否以保管有用行动的形式进行念念考？"。智能步地念念考必须处理几件纯推理模子大多不错幸免的事情：
决定何时住手念念考并选定行动
聘用调用哪个器具以及调用律例
融入来自环境的噪声或部分不雅测数据
在失败后纠正谈判
在屡次轮次和屡次器具调用中保握连贯性
智能步地念念考是一个通过行动进行推理的模子
5. Why Agentic RL Infrastructure Is Harder
为什么智能体强化学习基础关节更难
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.
一朝谈判从管制基准问题转向管制交互式任务，强化学习的架构便会发生变化。用于经典推理强化学习的基础关节已不及以应付这一需求。在推理强化学习中，你通常不错将采样轨迹视为大体自成一体的旅途，并配备相对明晰的评估器。而在代理强化学习中，计谋被镶嵌一个更大的框架之中：器具劳动器、浏览器、结尾、搜索引擎、模拟器、扩充沙箱、API层、内存系统以及编排框架。此时，环境不再只是静态的考据者；它已成为查验系统的一部分。
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.
这会创建一个新的系统要求：查验与推理必须已毕更绝对的解耦。若穷乏这种解耦，模子上线的浑沌量将大幅着落。试想一下，一个编码智能体需要针对及时测试框架扩充生成的代码：推理端会因恭候扩充反应而停滞不前，查验端则因穷乏已完成的轨迹而堕入饥饿景况，通盘活水线的运行遵守远低于基于经典推理的强化学习所预期的GPU利用率。要是再叠加器具延长、部分可不雅测性以及有景况环境等身分，这些低效问题便会进一步加重。其闭幕是，实验进程迟缓且充满灾祸，致使在你尚未达到谈判智商水平之前，就还是堕入窘境。
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.
环境自己也正成为一类一流的究诘器具。在SFT期间，咱们烂醉于数据的种种性；而在智能体期间，咱们则应烂醉于环境的质地：包括踏实性、真实性、覆盖范围、难度、景况种种性、反应丰富度、抗过拟合智商以及 rollout 生成的可蔓延性。环境构建已驱动成为一个果真的创业界限，而不再只是是副业神气。要是智能体正在给与查验，以合适雷同分娩环境的运行场景，那么环境便成了中枢智商栈的进攻组成部分。
6. The Next Frontier Is More Usable Thought
下一个前沿是更易用的念念维
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.
我的预期是，智能步地念念考将成为念念考的主导步地。我以为它可能最终取代大部分旧的静态独白式推理念念考：那种因穷乏交互而通过输出越来越多文原来抵偿的、过长的、镇定的里面轨迹。即使在尽头勤奋的数学或编码任务上，一个果真先进的系统也应该有权进行搜索、模拟、扩充、放哨、考据和纠正。谈判是矜重且高效地管制问题。
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.
查验这类系统时，最难办的挑战即是奖励舞弊。一朝模子取得了特兴味的器具拜访权限，奖励舞弊便会变得更加危境。具备搜索功能的模子可能会在强化学习过程中平直查找到谜底；编码智能体则可能利用仓库中的将来信息、糟蹋日记，或发现一些能卤莽绕过任务要求的捷径。要是环境中存在苦衷间隙，智能体看似阐述得超凡脱俗，实则是在被查验去舞弊。正因如斯，智能体期间比推理期间更加奥秘和复杂。更矫健的器具让模子变得更加有用，但同期也扩大了空虚优化的挫折面。咱们应预期，下一阶段的紧要究诘瓶颈将来自环境设想、评估器的鲁棒性、反舞弊机制，以及计谋与宇宙之间更具原则性的接口。尽管如斯，标的断然明确：借助器具的念念维模式远比镇定的念念考更有价值，也更有可能切实训导分娩力。
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.
智能步地念念考也将意味着对工程的独霸。中枢智能将越来越多地源自于多个代理的组织形式：一位厚爱策画与调度责任的统筹者，一群充任界限众人的专科代理，以及一群扩充更具体任务、同期协助截止高下文、幸免喧阗并保握不同端倪推理之间荫庇性的子代理。将来，咱们将从查验模子转向查验代理，再进一步从查验代理转向查验系统。
Conclusion
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.
推理海浪的第一阶段确立了一项进攻发现：在谈话模子之上应用强化学习，当反应信号可靠且基础关节能够支握时，可产生质地上更矫健的领路智商。
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.
深端倪的转机是从推理式念念维转向行动式念念维：从更永劫刻的念念考，转机为为了选定行动而进行的有序念念考。培训的中枢对象也随之发生了变化——如今，热心的焦点已不再是单纯的模子自己，而是"模子+环境"这一系统，更具体地说，是智能体过甚周围的生态系统。这使得哪些究诘后果最为枢纽也发生了改动：诚然，模子架构和查验数据依然至关进攻；但与此同期，环境设想、部署基础关节、评估器的矜重性，以及多个智能体之间协同互动所依赖的种种接口，也齐变得通常进攻。这也从头界说了"邃密念念考"的含义：在现实宇宙的敛迹条目下，最能握续推进行动的有用轨迹，而非单纯追求最长或最显眼的轨迹。
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.
它也改动了竞争上风的开端。在推理期间，上风来自更优秀的强化学习算法、更强的反应信号以及更高的可蔓延性。查验活水线。在智能体期间B体育(BSports)，上风将来自更优质的环境、更紧密的查验与劳动一体化、更矫健的模子敛迹工程，以及已毕模子有谈判与其所产生后果之间闭环的智商。
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