Together AI 推出 Together Post-Training 后训练服务
Together AI 发布名为 Together Post-Training 的新服务,支持通过环境奖励做强化学习、以及用监督数据做 SFT,来构建定制模型。公司称整套训练循环由用户自行定义,底层基础设施由平台负责协同优化,并支持 LoRA 训练(多个适配器可共享同一 GPU 容量)以及部分模型的全权重实验。首发支持 Muse Spark Open、GLM 5.3、Kimi K3 和 Qwen 3.8-27B 等开放权重模型,前八周按每 GPU 小时 9 美元的 B200 用量优惠计价。
该服务把强化学习后训练的基础设施交由平台托管,让企业无需自建大规模训练系统即可定制开放权重模型,这是 Together AI 从推理服务向训练服务延伸的一步。
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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