AWS makes SageMaker AI training sessions generally available
AWS announced the general availability of training sessions within Amazon SageMaker AI serverless model customization, letting developers write and coordinate their own post-training loop in Python — including custom loss functions or reward signals — while SageMaker AI handles GPU allocation, distributed execution, fault recovery and checkpoint storage. The launch supports gpt-oss-20b, gemma-4-31B-it, Qwen3.6-27B and Qwen3.8-27B, and AWS released a cookbook with ready-to-run SFT and RL recipes. AWS reports that a rank-32 LoRA adapter for GPT-OSS 20B trained for 50 steps on GSM8K rose from 0.46 to 0.60 training reward and from 40% to 50.5% held-out accuracy, figures the vendor supplied without independent replication.
AWS says the service gives teams control over post-training logic, such as custom losses and multi-turn RL reward shaping, without managing GPU clusters, a flexibility the company says customers asked for.
Companies getting real results from AI aren’t only using foundation models (FMs) off the shelf. They’re customizing open-weight models using post-training techniques such as reinforcement learning. A model trained on your business context, product catalog, or clinical vocabulary will be more accurate for your task and cost less to run. When you build agents, your data, workflows, evaluations, and success criteria also shape how the model behaves.
However, model customization is difficult. You must choose the right technique, set up and manage GPU infrastructure, and, for agents, build environments for multi-turn reinforcement learning. We launched Amazon SageMaker AI serverless model customization to solve these challenges, and today, we’re expanding it with the general availability launch of training sessions.
With training sessions you get control over the post-training logic without managing the underlying infrastructure. You write and coordinate the training loop, including custom loss functions or reward signals. SageMaker AI handles GPU allocation, distributed execution, fault recovery, and checkpoint storage.
Customize without the hassles of infrastructure management
With SageMaker AI serverless model customization, you don’t choose instance types, manage clusters, or write distributed training configuration.
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