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  "text": "New paper on giving LLM agents experience that improves the weights and stays readable at the same time.\n\nAgent-experience methods split into two camps. Externalized natural-language rules stay interpretable but drift out of sync with the policy. Parameter updates generalize but make weak local corrections under sparse rewards.\n\nJERP runs both off one trajectory stream, retrieving task-relevant rules at decision time and, after each episode, optimizing the policy while revising the rule pool against reference successful trajectories.\n\nThe conceptual payoff is the absorption dynamic. Stable, repeatedly useful behaviors get internalized into the weights over time, while the rule pool handles fresh local corrections. The interpretability-versus-generalization balance becomes a knob rather than an architecture choice.\n\nWhy does it matter?\n\nTeams want agents that both improve and stay inspectable. This is a clean template for getting both from the same trajectories. Gains land on AlfWorld and WebShop.\n\nPaper: https://t.co/avjHvESdBQ\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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