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EvoLib introduces a framework where large language models refine reusable skills and insights directly during inference without needing model updates or external labels. By transforming instance-specific observations into generalizable knowledge, the system enables AI agents to adapt and improve performance across tasks over time. This approach addresses the critical gap between simple memory retrieval and true learning evolution in deployed systems.

Sources coveringMicrosoft Research, AI Alignment Forum, arXiv, InfoQ

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EvoLib: Turning experience into evolving knowledge

Microsoft ResearchJuly 30, 20265 min read

LLMs do not get smarter just by remembering more. EvoLib turns experience into evolving knowledge, taking reusable skills and insights that help models learn and adapt across tasks long after deployment. The post EvoLib: Turning experience into evolving knowledge appeared first on Microsoft Research.

Self-supervised. EvoLib enables large language models to learn from their own experience during inference, without requiring ground-truth labels or external feedback. From experience to knowledge. EvoLib transforms past attempts into reusable skills and reflective insights that can be applied to future tasks. Knowledge that evolves. Useful skills and insights are continually refined, consolidated, and reweighted, turning instance-specific observations into increasingly general knowledge over time. Learning that transfers across tasks. By turning experience into reusable knowledge, EvoLib helps AI models learn from past successes and failures and evolve the knowledge that has the highest potential on improving…

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