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This repository contains the dataset associated with the paper EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle.
EvolveR is a framework designed to enable LLM agents to self-improve through a complete, closed-loop experience lifecycle. This lifecycle comprises two key stages: (1) Offline Self-Distillation, where interaction trajectories are synthesized into reusable principles, and (2) Online Interaction, where the agent retrieves these principles to guide decision-making. The effectiveness of this approach is demonstrated on complex multi-hop question-answering benchmarks.
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Paper for Edaizi/EvolveR-NQ-HotpotQA
Paper • 2510.16079 • Published • 1