Community
Developers can now reduce inference costs and latency for agent-based applications by fine-tuning smaller models on SageMaker, while business stakeholders can justify broader agentic AI deployment by trading frontier model costs for fine-tuned smaller models without sacrificing reliability.
AWS published a guide on fine-tuning LLM-powered search agents using multi-turn reinforcement learning (MTRL) on Amazon SageMaker AI. The approach teaches smaller models to reliably use tools and environments, achieving frontier-model quality at lower latency and cost, with measured improvements in retrieval quality and reliability.
Read the full article at AWS AI/ML
CoFabrix summarises and comments on this story. The original reporting belongs to AWS AI/ML.
Find out where your organization stands -- and what to do about it.