EP324: JERP synchronizes AI rules and neural weights

Title: Joint Learning of Experiential Rules and Policies for Large Language Model AgentsSource: http://arxiv.org/abs/2606.27136v1Summary:This work introduces JERP, a novel agentic framework that simultaneously updates an external pool of natural-language rules and the model's parametric policy from the same interaction trajectories. By keeping prompt-based rules synchronized with the evolving policy, it establishes a unified paradigm for optimizing both interpretive guidance and internal capabilities in multi-step interactive environments.