AWS launches general availability of SageMaker AI training sessions for custom model fine-tuning
AWS announced general availability of training sessions within Amazon SageMaker AI serverless model customization. Teams can write their own post-training loops — including custom loss functions and reward signals for reinforcement learning — while SageMaker AI handles GPU allocation, distributed execution, fault recovery, and checkpoint storage. Supported techniques include SFT, DPO, RLVR, RLAIF, and multi-turn RL, and users can choose LoRA or full fine-tuning. The feature supports models including gpt-oss-20b, gemma-4-31B-it, Qwen3.6-27B, and Qwen3.8-27B, and billing is based on tokens processed, pausing while a user's reward function scores responses.