Memory-Augmented Learning for LLM Agents Without Fine-Tuning
LLM agents can now learn continuously from experience, without a single parameter update. Our latest research at Megagon Labs introduces a memory-driven framework that enables agentic systems to improve from feedback without the cost, inflexibility, and opacity of fine-tuning. Why Fine-Tuning Falls Short Traditional approaches to improving LLM performance rely on parameter updates, which are […]
