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

- Company: Together AI (together.ai)
- Announced: 2026-10-19
- Category: developer-tool-release
- Coverage: not counted
- Announcement: yes
- Group: announcements
- Source: https://www.together.ai/blog/together-post-training
- Record: https://forck.live/items/24698-together-post-training-frontier-grade-model-shaping-the-way-that-fits-you
- Subject: Inference platform
- Models affected: Muse Spark Open, GLM 5.3, Kimi K3, Qwen 3.8-27B

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 the service lets customers define the training loop while Together co-optimizes the systems and infrastructure underneath for model quality and training speed. Together argues that training custom models is no longer niche, as companies seek to cut costs and improve task-specific performance, and that many that could benefit never start or stall because post-training demands repeated work on data curation, training algorithms and evaluation. Experiments run on Model Resources, dedicated GPU allocations that are not shared with other users' requests, and each resource can hold one or several Training Sessions tied to a single base model. Together says users can run LoRA training, including multiple LoRA adapters sharing the same GPU capacity, as well as full-weight experiments for select models, and can start a model resource with zero generators for SFT to save costs. The service supports standard losses including cross-entropy, GRPO, CISPO and DPPO, and allows computing a custom training objective locally and sending its gradients to the Trainer. It launches with open-weight models including Muse Spark Open, GLM 5.3, Kimi K3 and Qwen 3.8-27B, with the selection to expand in the coming weeks.

## Evidence

Verbatim from https://www.together.ai/blog/together-post-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.

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