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

Memory systems have shown promise for improving agent performance, but their potential remains largely unexplored for small language models, which struggle to generate sufficient successful trajectories on their own. We propose Agent Memory Distillation (AMD), a training-free framework that transfers structured knowledge from a large teacher agent to a small student agent through hierarchical memory. AMD constructs three complementary memory types from successful teacher trajectories: Workflow m

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ethics.ai (6 August 2026), “Agent Memory Distillation: Empowering Small LLM Agents with Hierarchical Teacher Memory,” evidence record 17967, https://ethics.ai/record/17967 (originally published by HuggingFace Daily Papers).

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