我们正在推出 " 共同培训后 "，这是在我们平台上建立定制模式的新服务，既通过RL提供环境奖励，又通过SFT提供监督数据。 这一服务旨在为您提供灵活而高效的模型塑造，而无需承担沉重的下层负担。
共同开展培训后工作，很容易确定培训周期，同时我们合作优化下方的系统和基础设施，以达到模式质量和培训速度。 现在没有什么能挡住你和独一无二的模特儿
定制模型的价值
2026年，针对你AI工作量的培训定制模式不再是一个特色话题。 公司积极探索降低成本，提高具体任务绩效。 许多AI-native和AI-forward组织不是通过上下文或利用工程来固定模型行为并提高其效率，而是在培训专家agents，以成本和运行时间的一小部分来匹配前沿模型的质量。 除此之外，培训模式本身也会产生更大的自主权和灵活性：由此形成的检查站可以部署在任何地点、进一步完善甚至在当地使用。
在"一起"中，我们希望赋予每个AI开发者以能力，尽可能方便地获得完成任务的最佳模型。 后训练一个伟大的模型需要很多关于数据解析、培训算法和评价协议的迭代。 除此之外，RL的基础设施越来越复杂，在“效率优化”和“阻碍你运行的沉默错误”之间有细线。 可靠和大规模地为大型模型这样做是其自身的一套挑战。
边疆实验室有整个团队解决上述每个问题。 由于同时应对所有挑战是一项巨大的投资，许多可以从培训后受益的公司从来就没有开始，也没有开始和停顿。 集训后处理所有优化和基础设施而不受阻碍，我们前方部署的团队与……合作我们的客户帮助完成端到端的工作流程。 这让每个研究者或领域专家都专注于他们最擅长的，而我们则关注其他的。
一眼看一眼后培训
随着后培训的复杂性继续演变，围绕后培训的工具也应不断演变。 共同后训练为ML开发者提供了一套熟悉的常见抽象，以及对每个实验所使用的资源的控制，同时保证罩下的所有计算都是快速的，成本高效的，最重要的是正确的。
我们建立了我们的服务为您的实验提供最高程度的灵活性。 你可以运行LoRA训练，包括多个共享相同GPU容量的LoRA适配器，以及用于选定型号的全重实验。 与我们的微调服务形成对比的是，"共同训练后"的整个训练循环由你来定义。 您可以使用任何适合您设置的最佳数据、环境或代理工具，并在整个培训过程中添加您需要的任何步骤(如记录或外部基准)。 整个工作流程如下：
所有实验都是在Model Resources上进行的——GPU分配是完全属于你的，而不是与其他人的优先权请求竞争。 每个模型资源可以举办一个或几个培训班，每个培训班对应使用同一基模型的单一实验(例如一组LoRA适配器). 这意味着你得到最好的推出和训练性能，因为你从不与其他用户的请求竞争，执行相同的硬件。 同时，专用的GPU分配意味着可以在适应大型知识密集型任务时运行全重微调，超越LoRA.
一旦分配模型资源并开始会话，就可以执行训练循环的每个步骤。 每期培训班都有一个培训员实例，负责向前/后退步骤和更新权重，以及用于执行政策推出的发电机实例。 对于每一个模型，我们大力优化培训和推出的性能，确保所有操作都快速，在数字上相互配合。 在SFT的情况下，可以以0发电机启动模型资源，以节省成本。 您决定将导师参数发送到生成器的点；我们也支持同步的RL，用于在任何时候都高使用两个组件的GPU，特别是长剂推出。
虽然我们支持一些标准损失(从跨大陆和GRPO到CISPO和DPPO)，我们也允许在本地计算一个自定义训练目标，并将其梯度发送给训练员计算更新。
集训后发布与一系列领先的开放量级模型，包括Muse Spark Open,GLM 5.3,Kimi K3，以及Qwen 3.8-27B. 我们将在未来几周扩大选择范围，确保您既能使用能力最高的前沿LLMs，也能使用高效的小型模型。
质量、业绩和效率
下放培训后基础设施的内部权力可能是一项令人生畏的决定。 我们的团队有在复杂的机器学习系统中捕捉无声bug的第一手经验，从推动RL研究的前沿到训练服务过数百万用户的LLMs. 我们理解，最好的培训堆是给你最好的模型，而建设这种基础设施需要在所有组成部分认真实施和共同设计。
由于这些原因，我们把产出模型的质量作为我们开发这项服务的北星。 影响RL结果的问题的一个臭名昭著的例子是训练引擎和推论引擎之间的调和：如果相同的模型重量由于数字不匹配而产生略微不同的输出，整个运行可能表现不佳甚至崩溃。 我们通过培训和推论，利用 " 推出游轮回放 " 和我们关于培训稳定性的研究所推动的先进优化等标准技术，消除许多政策流转来源。
除了模型质量之外，我们还努力使每个运行都尽可能快和高效，因为后训练需要多个迭代：运行完成得越快，成本越低，你得到测试的想法就越多。 RL的大部分时间都花在制作推论上，我们的推出引擎利用同样的优化与我们的服务堆。 对于多转弯的agents，智能缓存和路由将冗余计算最小化，同时保持内存成本的界限。 在训练方面，优化了平行主义和顺序包装，同时加速了SFT和RL两个步骤。
结合后训练，你每个实验大小到它需要的，不付更多。 如果你的实验被推出的货币束缚了你可以要求样本复制品的确切数量 LoRA的几个实验可以共享一个模型资源，因此进行扫描并不意味着为每个设置保留更多的GPU. 我们每GPU分钟花费每个模型资源，然而，由于我们许多优化我们的专用和多LoRA服务堆，培训的总成本下降低于象征性定价当你扩大实验。
培训后优化部署
当培训后试验完成后，也许必须同时运行每个产生的模型，以比较其产出。 我们现在支持多LoRA适配器在专用模型推论中服务：你不是为每次运行开始单独的生产部署，而是可以在方便的时候将适配器互换为单一的GPU部署，帮助你降低服务成本。
最后，将模型转换为生产中最高的吞吐量，意味着它有可能以较低的精度、不同的硬件配置以及不同的内核与推出生成相比执行。 通过算法和系统改进相结合，我们确保每个训练检查站保留服务质量，即使在从多LoRA采样转换为FP4精度的完全性能综合推论后也是如此。
开始
为了开始你训练后的第一次实验安装最新的SDK 并读取文件我们亦提供一些烹饪书籍，这些烹饪本提供了使用GRPO进行标准GSM8K 基准测试培训的例子，将DPO作为自定义损失执行，或者在同一模型资源内培训多个LoRA适配器。 如果你已经有一个使用培训后服务如Tinker API的设置，我们也提供了一种简单的迁移方法。 你可以替换你的剧本中的一行，其他的都可以保持不变：
阅读我们的叮当移民指南学习更多。
AI-本土公司今天如何使用 " 共同培训后 "
在发布之前，我们与一些组织合作，推动定制模式的前沿。 在过去几个月里，我们允许他们尽早获得我们的服务；我们收到的反馈对于开发现在每个人都可以使用的产品来说是宝贵的。 听取各小组本身的意见：
“我们从第一天起就对共同培训后平台有丰富的经验。 高度的控制和灵活性让我们在几个小时内开始进行真正的RL实验。 服务效率很高，使我们能够在同一GPU容量上同时进行5次RL实验。 " 共同研究小组在整个培训过程中是一个杰出的合作者，为我们提供关于模式选择、培训技术、奖励职能和培训效率的反馈。
- Anthony Li,Deel数据科学副主任
“在Jack & Jill，我们建造了两个AI agents：专业的Jack，我们用“共同训练后”来进行训练，登机很简单，他们的团队帮助设计了我们的实验，他们的前方部署的工程师们写了剧本，使我们获得了第一结果。 使用开源模式
— 奥利弗·诺曼德，杰克·吉尔创始工程师
“我们自4月份培训后，随着最新的迭代，无缝自动登机使得快速发布全程代理RL运行变得容易。 实践证明，培训后服务具有特别的直觉性、灵活性和影响力，可以扩大复杂的代理工作量。”
- Adit Jain，研究员，Collinear AI
与培训后共同建设
将前沿模式定制化为可靠、灵活、高效的服务。 下一步是让你好好利用在头8个星期，我们正在提供 " 共同培训后 "，价格降低为每GPU小时9美元，使用B200。 学习如何开始今天的工作
你不必独自行动。 对于想要搭档的公司，我们的前方部署的工程师与你的团队一起工作在训练后的整个生命周期。 他们首先会帮助选择合适的开放模式和 agent 工具，与您合作开发您的数据和培训环境
如果你愿意与Google合作为你的组织训练模式，请随意接触我们。
本文由 AI宝盒自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
We are introducing Together Post-Training, a new service for building custom models on our platform — both from environment rewards via RL and supervision data via SFT. This service is designed to give you flexible and efficient model shaping without the heavy infra burden.
Together Post-Training makes it easy to define the training loop, while we co-optimize the systems and infrastructure underneath for model quality and training speed. Nothing now stands between you and the best model that is uniquely yours.
The value of custom models
In 2026, training custom models for your AI workloads is no longer a niche topic. Companies are actively exploring it to reduce costs and improve task-specific performance. Instead of fixing model behaviors and improving their efficiency via context or harness engineering, many AI-native and AI-forward organizations are training specialist agents, matching the quality of frontier models at a fraction of the cost and runtime. On top of that, training the model yourself yields greater ownership and flexibility: the resulting checkpoint can be deployed in any location, further refined, or even used locally.
At Together, we want to empower every AI developer with the ability to get the best model for their task, as conveniently as possible. Post-training a great model requires many iterations on data curation, training algorithms, and evaluation protocols. On top of that, infrastructure for RL is getting ever more complex, with a fine line between “an efficiency optimization” and “a silent bug that holds back your runs”. Doing so for large models reliably and at scale presents its own set of challenges.
Frontier labs have entire teams solving each of the above problems. Since tackling all challenges at once is a big investment, many companies that could benefit from post-training never start it, or start and stall. Together Post-Training handles all optimizations and infrastructure without getting in the way, and our forward-deployed team partners with our customers to help with the end-to-end workflow. This lets every researcher or domain expert focus on what they do best while we take care of the rest.
Together Post-Training at a glance
As the complexity of post-training continues to evolve, so should the tooling around it. Together Post-Training provides a set of common abstractions familiar to ML developers, as well as control over the resources used by each experiment, while making sure that all computations under the hood are fast, cost-efficient, and, most importantly, correct.
We built our service to offer the highest degree of flexibility for your experiments. You can run LoRA training, including multiple LoRA adapters sharing the same GPU capacity, as well as full-weight experiments for select models. In contrast to our fine-tuning service, the entire training loop of Together Post-Training is yours to define. You can use any data, environments, or agentic harnesses that suit your setup best, and add any steps you need (such as logging or external benchmarking) throughout the training process. The entire workflow is as follows:
All experiments run on Model Resources — GPU allocations that are fully yours, not competing with others’ requests for priority. Each Model Resource can hold one or several Training Sessions, where each one corresponds to a single experiment using the same base model (for example, one set of LoRA adapters). This means you get the best rollout and training performance, as you never have to compete with other users’ requests executing on the same hardware. At the same time, dedicated GPU allocations mean you can run full-weight finetuning, going beyond LoRA when adapting to large knowledge-intensive tasks.
Once you allocate the model resource and start the session, you can execute each step of the training loop. Each session contains a Trainer instance responsible for running forward/backward steps and updating the weights, as well as Generator instances for executing rollouts from the policy. For every model, we heavily optimize training and rollout performance, making sure all operations are fast and numerically aligned across stacks. In the case of SFT, you can start a model resource with 0 generators to save costs. You decide the point at which the trainer parameters are sent to the generator; we also support asynchronous RL for high GPU utilization of both components at all times, especially with long agentic rollouts.
While we support a number of standard losses (from cross-entropy and GRPO to CISPO and DPPO), we also allow computing a custom training objective locally and sending its gradients to the Trainer for computing the updates.
Together Post-Training launches with a range of leading open-weight models, including Muse Spark Open, GLM 5.3, Kimi K3, and Qwen 3.8-27B. We will expand the selection in the coming weeks, making sure you can use both the highest-capability frontier LLMs and efficient smaller models.
Quality, performance, and efficiency
Delegating the internals of post-training infrastructure can be a daunting decision. Our team has first-hand experience catching silent bugs in complex machine learning systems, from pushing the frontier of RL research to training LLMs that have served millions of users. We understand that the best training stack is the one that gets you the best model, and building such infrastructure demands careful implementation and co-design across all components.
For these reasons, we prioritized quality of output models as our north star in the development of the service. One notorious example of issues affecting RL results is the alignment between training and inference engines: if the same model weights produce slightly different outputs due to mismatching numerics, the whole run might underperform or even collapse. We eliminate numerous sources of policy drift across training and inference, using both standard techniques like Rollout Routing Replay and advanced optimizations driven by our research in training stability.
Beyond model quality, we also work to make each run as fast and as efficient as possible, because post-training requires multiple iterations: the sooner a run finishes and the less it costs, the more ideas you get to test. Most of the time in RL is spent on generating rollouts, which allows us to capitalize on years of research and engineering behind Together’s inference products. Our rollout engines draw on the same optimizations as our serving stack. For multi-turn agents, intelligent caching and routing minimize redundant computations while keeping memory costs bounded. On the training side, optimized parallelism and sequence packing speed up both SFT and RL steps.
With Together Post-Training, you size each experiment to what it needs and do not pay more. If your experiment is bottlenecked by rollout concurrency, you can request the exact number of sampler replicas. Several LoRA experiments can share one model resource, so running a sweep doesn’t mean reserving more GPUs for every setup. We bill every model resource per GPU-minute, yet thanks to many optimizations of our dedicated and multi-LoRA serving stacks, the overall costs of training drop below token-based pricing as soon as you scale up the experiments.
Optimized deployment after training
When the post-training experiments are finished, it might be important to run each resulting model side by side to compare their outputs. We now support multi-LoRA adapter serving in Dedicated Model Inference: instead of starting a separate production deployment for each run, you can swap the adapters at your convenience in a single GPU deployment, helping you lower the serving costs.
Lastly, converting the model to run at the highest throughput in production means it potentially executes in lower precision, in a different hardware configuration, and with different kernels compared to rollout generation. Through a combination of algorithmic and systems improvements, we ensure that each training checkpoint retains the quality in serving, even after switching from multi-LoRA sampling to a fully performant merged inference in FP4 precision.
Getting started
To begin your first post-training experiments, install the latest Together SDK and read the docs. We also provide a selection of cookbooks that can help you get started with Together Post-Training, which are available at github.com/togethercomputer/post-training-cookbook. These cookbooks provide examples of training with GRPO on a standard GSM8K benchmark, implementing DPO as a custom loss, or training multiple LoRA adapters within the same model resource. If you already have a setup that uses post-training services such as Tinker API, we also offer a simple way to migrate from them. You can replace one line in your scripts, and everything else can stay the same:
Read our Tinker migration guide to learn more.
How AI-native companies are using Together Post-Training today
Before this launch, we worked with a select group of organizations pushing the frontier of custom models. We granted them early access to our service over the past few months; the feedback we received was invaluable in developing the product which now everyone can use. Hear from the teams themselves:
“We had a great experience with the Together Post-Training platform from day one. The high degree of control and flexibility let us start running real RL experiments within a couple of hours. The service is efficient, allowing us to run up to five concurrent RL experiments on the same GPU capacity. The Together Research team was an exceptional collaborator throughout the training process, giving us feedback on model selection, training techniques, reward functions, and training efficiency.”
– Anthony Li, Associate Director of Data Science, Deel
“At Jack & Jill, we build two AI agents: Jack for professionals, and Jill for companies, who recruit from Jack’s network of professionals who’ve opted in to being introduced. We used Together Post-Training to post-train a model that predicts which candidates a hiring manager will want to meet, a core step in Jill’s search. Onboarding was easy, their team helped design our experiments, and their forward-deployed engineers wrote the scripts that got us to first results. We matched a closed-source model’s quality in offline evaluations, using an open-source model that’s significantly cheaper to serve.”
– Oliver Normand, Founding Engineer, Jack & Jill
“We’ve been collaborating with the Together team since the launch of its Post-Training beta in April. With the latest iteration, seamless self-onboarding made it easy to quickly launch full-scale agentic RL runs. The Post-Training service has proven to be exceptionally intuitive, flexible, and powerful for scaling sophisticated agentic workloads.”
– Adit Jain, Researcher, Collinear AI
Build with Together Post-Training
With the launch of Together Post-Training, we’re turning frontier-grade model customization into a reliable, flexible and efficient service. The next step is for you to make the most of it. For the first eight weeks, we are offering Together Post-Training at a reduced price of $9 per GPU-hour of B200 usage. Learn how to get started today — we’re excited to see what you will build.
But you don’t have to do this alone. For companies that want a partner, our forward-deployed engineers work alongside your team across the full post-training lifecycle. They’ll start with helping select the right open model and agent harness for your use case, work with you on your data and training environments, and then help with evaluations and shipping your own optimized endpoint.
If you would like to collaborate with Together on training models for your organization, feel free to reach out to us.
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