Post-training supports custom training loops with SFT, DPO and GRPO, billed per GPU-minute

Post-training lets you write your own training loop and run it against Together AI GPUs. Your code builds each batch and chooses the loss function and learning rate, while the GPUs you provision run the forward and backward passes, optimizer steps, and sampling. It bills per GPU-minute.

Write a loop with the Python SDK (together.post_training, version 2.41.0 or later), or start from a recipe in the post-training cookbook.

What's included:

• Three training methods: Supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement learning with group relative policy optimization (GRPO). Each method trains a low-rank adapter (LoRA) or the full model weights.
• Custom loss functions: Train with any objective you compute in PyTorch from the model's log-probabilities.
• Multi-turn environments: Train tool-use agents with GRPO over whole episodes.
• Several adapters on one base model: Attach several LoRA sessions to one model resource, so a hyperparameter sweep costs one base model's GPU time.
• Multi-LoRA deployment: Attach trained adapters to one dedicated deployment of their base model, then name an adapter in each request to compare them.
• Prepare for FP4 inference: Merge a LoRA adapter into GLM-5.3 or Kimi-K3 and quantize the result to FP4 for dedicated model inference.
• Tinker compatibility: Run a Tinker training script on post-training by changing its import line and, if needed, its base model.
• CLI commands: Together CLI 2.41.0 adds tg training to list model resources, sessions, and checkpoints and to run FP4 preparation jobs. tg beta endpoints deploy adds --attach-adapter and --merge for deploying a trained adapter.

See Post-training for details.