ShoppingBench

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1. 3d
   AWS tutorial: distill a shopping agent with sequence-level knowledge distillation

   The AWS Machine Learning Blog shows how to build a lower-cost shopping agent on Amazon SageMaker AI using sequence-level knowledge distillation (SeqKD), generating multi-step trajectories with a frontier teacher on Bedrock, keeping only those that solved the task with ShoppingBench's scorer, and fine-tuning a smaller student on serverless infrastructure. The post also covers encoding trajectories as chat-formatted messages, tracking training with MLflow, gating quality with SageMaker AI model evaluation and a custom tool-selection scorer, and serving the student behind an endpoint that runs the tool loop. AWS reports the payoff was a 4B student that recovers essentially all of its frontier teacher's task quality.

   AWS Machine Learning Blog · 🔥 0

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