Fine-tuning LLM Agents without Fine-tuning LLMs: Skill Transfer via Memory Augmentation
Memory architecture enables zero-shot skill transfer across agents, achieving 87.88% on GAIA validation without model updates.
Tired of fine-tuning LLMs for every agent tweak? This paper shows how to ‘fine-tune agents’ via external memory banks of distilled skills. Agents query embeddings for tool-use patterns and handoffs, preserving base model generality.
What changed. Skill transfer via memory hits GAIA 87.88% pass@3 and 95% SimpleQA, rivaling full fine-tunes.
Huge for builders: upgrade agents in hours, not weeks. Perfect for multi-agent fleets needing specialized memory without retraining. Read the paper.