AgentBrew: Lifelong Knowledge Brewing from Strong Teachers to Weak LLM Agents
AgentBrew introduces a method for transferring knowledge from a strong teacher LLM to a weaker student agent by distilling interactive experiences into an external memory, without requiring weight updates or teacher access at test time. The approach uses failure-triggered teacher interventions and student-aware synthesis to generate actionable, environment-validated guidance. Evaluations across coding, math, and tool-use tasks indicate that this method produces capable and deployable LLM agents.
Why it matters: This approach could make it easier and more cost-effective to deploy capable LLM agents by eliminating the need for continual model retraining or ongoing teacher involvement.
Full story at: arXiv AI/ML ↗