AgentBrew: Lifelong Knowledge Brewing from Strong Teachers to Weak LLM Agents

Abstract:Deploying LLM agents typically requires a compact test-time student, even if a stronger teacher is available during training. We study knowledge brewing: distilling a teacher's interactive experience into a persistent external memory for the student. Crucially, this requires no weight updates, expert demonstrations, ground-truth labels, or test-time teacher access. This setting poses two challenges: environments provide only sparse, binary feedback, and teacher-authored notes must be inherently tailored to be concretely executable by a substantially weaker student. To address these hurdles, we propose AgentBrew, comprising two coupled components. First, a failure-triggered teacher--Ralph Loop mitigates sparse feedback by transforming student failures into environment-validated notes. Second, student-aware synthesis calibrates teacher knowledge to the weak executor's operational granularity, yielding model-specific, actionable guidance. Extensive evaluations and comprehensive ablations across coding, math, and tool-use tasks demonstrate that this asymmetric, training-free brewing paradigm produces highly capable yet deployable LLM agents.

Artificial Intelligence (cs.AI)

arXiv:2607.16851 [cs.AI]

(or arXiv:2607.16851v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.16851 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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