Agent Memory Distillation: Empowering Small LLM Agents with Hierarchical Teacher Memory

Proposes a training-free framework that distills a large teacher agent's successful trajectories into hierarchical memory (workflow, subtask, and function-level) for small student agents, injecting workflow/subtask memory proactively and function memory reactively on tool-call errors. Tested with 4B-8B student models against a GPT-5-mini teacher, giving accuracy gains up to 27.2 points over baselines on tool-use benchmarks.