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JERP introduces a method for LLM agents to jointly learn interpretable natural-language rules and update policy parameters from the same interaction trajectories, improving performance on AlfWorld and WebShop while maintaining inspectability.
This paper proposes the EDV framework, which uses multiple heterogeneous agents in execute-distill-verify stages to build reliable experiences for LLM agents, preventing self-confirmatory errors and improving performance on long-horizon benchmarks.
ExpGraph is a model-agnostic framework that enables LLM agents to reuse past experiences via a self-evolving graph of skills and failures, improving task performance by 12–21% without retraining the executor.