RLxF: RL from World Feedback

This workshop explores a shift beyond human preference signals by treating world feedback 🌍 —measurable signals from real-world interactions such as efficiency, safety, health, performance, and economic outcomes—as a first-class training signal for reinforcement learning systems. The goal of this workshop is to move beyond human feedback to train reinforcement learning systems using world-grounded learning signals (e.g., efficiency, safety, and economic outcomes) that better reflect the true consequences of agent behavior. Bringing together researchers across reinforcement learning, foundation models, robotics, systems, and AI alignment, it focuses on how to model and integrate heterogeneous, noisy, and delayed feedback into modern learning pipelines. Through invited talks, contributed papers, and interactive panels, the workshop aims to clarify core challenges, develop shared frameworks, and advance scalable, robust, and deployable learning paradigms grounded in real-world consequences.