MaxText Broadens Post-Training Support: SFT and RL Now Available on Single-Host TPUs – Google Developers Blog Wei Wei Developer Advocate. Weiren Yu Product Manager. In the fast-changing world of large language models (LLMs), pre-training represents just the initial phase. Post-training is crucial for converting a base model into a specialized assistant or a high-performing reasoning system. Today, we’re thrilled to introduce new capabilities in MaxText that simplify this workflow: Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) are now supported on single-host TPU setups, including v5p-8 and v6e-143. Powered by JAX and the Tunix library, MaxText delivers a high-performance, scalable solution for developers to enhance their models with state-of-the-art post-training methods. You can dive into the complete SFT and RL documentation to begin your post-training journey on TPUs right away. Supervised Fine-Tuning (SFT): Precision Tuning Made Simple. The main approach for tailoring a pre-trained model to adhere to particular instructions or perform exceptionally well on specialized tasks is Supervised Fine-Tuning.
Google Developers Blog