Fri, Jul 10, 2026 8:00 AM–5:00 PM KST @ Grand Ballroom 101-102

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.

Speakers

Organizers

Submission Instruction and Timeline

We will use OpenReview to manage the submissions and the double-blind review process. 

Format

• Full-paper submission (up to 9 pages) in ICML or NeurIPS format with potentially large-scale experiments

• Short submission (2-4 pages) in ICML or NeurIPS format with proof-of-concept demonstrations of the idea proposed. The proof-of-concept submission can include demos, code, and a blog post. This format is to account for underrepresented researchers who may not have access to large amounts of compute

Note

• Page limits exclude references and appendices.

• The workshop proceedings are non-archival, and we welcome submissions that are currently under review at other venues (e.g., NeurIPS 2026).

Key dates

• Submission deadline: May 13, 2026, AoE

• Author notification: May 31, 2026, AoE

• Camera-ready deadline: June 30, 2026, AoE

• Workshop date: July 10, 2026

Contact

rlxf2026@gmail.com