Deploying Multi-Turn RL Infrastructure for Amazon Nova on Amazon SageMaker HyperPod | Amazon Web Services
When you build enterprise agents that execute multi-step workflows, you face a fundamental training challenge. These agents query databases, call APIs, cross-reference results, and recover from mid-process failures. The quality of any single action depends on what happens several steps later.
Standard reinforcement learning from human feedback (RLHF) optimizes single responses in isolation. This approach falls short for multi-step workflows where an agent that validates data before proceeding prevents a cascade of downstream errors. Multi-turn reinforcement learning (RL) addresses this gap by optimizing over entire interaction sequences. Your agents learn tool orchestration, error recovery, and multi-step reasoning through trial and error. Supervised fine-tuning (SFT), retrieval-augmented generation (RAG), and continued pre-training are complementary techniques, but they typically do not teach these sequential decision-making capabilities on their own.
Amazon SageMaker AIalso offers multi-turn RL as a fully managed, serverless capability, bringing this technique to SageMaker training jobs with no infrastructure...
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