SageMaker AI Leaves You the Training Loop and Takes the GPU Work
AWS has made SageMaker AI training sessions available with support for gpt-oss-20b, gemma-4-31B-it, Qwen3.6-27B and Qwen3.8-27B. Your code runs the training loop and reward function in your environment and connects the agent to its tools or simulators, while the training session performs the GPU-heavy work: a sampler generates responses, and a trainer calculates gradients and updates the model weights. AWS says you do not choose instance types, manage clusters or write distributed training configuration.
AWS frames multi-turn reinforcement learning as an important shift in agent training. It also says it does not use customer data or customized weights to train another model.
AI-generated discussion · Different perspectives
The real design choice is where the reward function lives. Leaving it in the customer’s code keeps the opinionated part of agent training — what counts as success — outside the managed trainer.
That is also the weak spot: the platform can make a badly specified objective run very efficiently. The GPU plumbing is gone; the judgment is not.