Together Post-Training: frontier-grade model shaping, the way that fits you

Together Post-Training: frontier-grade model shaping, the way that fits you Webflow Analyze/Optimize tracking bridge -->

Introducing Together Post-Training: Better models, faster runs, lower costs →

Introducing Together Link: Run open models inside the harness you already use →

The Open Source AI Stack: models, inference, routers, harnesses & tools →

GLM-5.3 vs. GLM 5.3 Flash on DeepSWE →

⚡ On-demand B200s now available on Together GPU Clusters →

All blog posts

Fine-Tuning

Published 10/19/2026

Together Post-Training: frontier-grade model shaping, the way that fits you

Authors

Ivan Provilkov, Gleb Khaykin, Denis Mazur, Nikita Smetanin, Egor Timofeev, Conner Manuel, Felix Zhang, Alex Mazitov, Jasmine Li, Gleb Vazhenin, Sergei Vorobyov, Qingyang Wu, Derek Petersen, Arseniy Belorukov, Artem Chumachenko, Aman Singhal, Alpay Ariyak, Jisen Li, Vladimir Malinovskii, Maksim Abraham, Zhongzhu Zhou, Denis Fedorenko, Sonny Khan, David Wang, Jennifer Wu, Max Ryabinin

Table of contents

40+ Models Chosen for Production...40+ Models Chosen for Production...40+ Models Chosen for Production...

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:

A high-level summary of using Together Post-Training. Left: commands executed on the user side, right: service components running on Together’s side. 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.

Example of training with a custom loss from Together Post-Training docs 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...