Best practices for multi-turn reinforcement learning in Amazon SageMaker AI

Short summary

AWS outlines best practices for multi-turn reinforcement learning in SageMaker: building reliable training environments, external evaluation frameworks, reward functions aligned with end tasks, handling multi-turn agent state, and monitoring key metrics to drive iteration.

• •Build trustworthy training environments and external evaluation systems
• •Design rewards aligned with end-task objectives and manage multi-turn agent state
• •Monitor key metrics to determine when to iterate on RL training approach

Generated with AI, which can make mistakes.