Together Post-Training launches for custom open-model tuning
Ownership of the resulting model weights is the differentiator here, since most hosted fine-tuning services leave users with an API endpoint rather than a portable asset. Teams building proprietary capabilities on open models can bring their own reward functions and environments rather than accepting a fixed RL recipe. It positions Together as an alternative to platform-locked tuning offerings from larger cloud providers.
Together AI announced that Together Post-Training is now live. The service lets users turn open models into custom models they own, using supervised fine-tuning (SFT) or reinforcement learning (RL). Customers define their own data, rewards, environments and training loop, while Together runs the underlying infrastructure tuned for quality and speed.
Together Post-Training is live
Turn open models into custom models you own, with SFT or RL.
You define the data, rewards, environments and the training loop itself. We run the hard infrastructure underneath it, tuned for quality and speed.