Together AI launches Together Post-Training service for RL and SFT
Together AI introduced Together Post-Training, a service for building custom models on its platform through reinforcement learning from environment rewards and supervised fine-tuning on supervision data. The company says users define the entire training loop while Together co-optimizes the underlying systems and infrastructure, with support for LoRA training (including multiple adapters sharing GPU capacity) and full-weight experiments for select models. It launches with open-weight models including Muse Spark Open, GLM 5.3, Kimi K3 and Qwen 3.8-27B, priced at a reduced $9 per GPU-hour of B200 usage for the first eight weeks.
By hosting the reinforcement-learning post-training stack, Together offers companies a way to shape open-weight models without building large-scale training infrastructure themselves, extending the company from inference serving into training.
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.
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