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On October 23, 2026, AWS announced that training sessions for Amazon SageMaker AI serverless model customization are now generally available. Users can write and coordinate their own training loops, including custom loss functions and reward signals; GPU allocation, distributed execution, fault recovery, and checkpoint storage are handled by SageMaker AI. The feature supports SFT, DPO, RLVR, RLAIF, and multi-turn RL, with a choice of LoRA or full fine-tuning, and currently supports gpt-oss-20b, gemma-4-31B-it, Qwen3.6-27B, and Qwen3.8-27B. Billing is based on tokens processed by SageMaker AI; billing stops when the reward function scores in the user's environment, and no reserved capacity is required.
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AWS launches training sessions GA for SageMaker AI serverless model customizationDevelopments
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- Source:AWS Machine Learning
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Oct 23
AWS Machine LearningFirst-partySelected
AWS launches training sessions GA for SageMaker AI serverless model customization
AWS announced that training sessions for SageMaker AI serverless model customization are generally available; users can write and orchestrate their own training loops, including custom loss functions and reward signals, while SageMaker AI handles GPU allocation, distributed execution, fault recovery, and checkpoint storage. The feature supports SFT, DPO, RLVR, RLAIF, and multi-turn RL, with a choice of LoRA or full fine-tuning, and currently supports gpt-oss-20b, gemma-4-31B-it, Qwen3.6-27B, and Qwen3.8-27B. Billing is per token processed by SageMaker AI, billing stops when the reward function scores in the user's environment, and no reserved capacity is required.