{"total":7,"rows":[{"id":"2505.23150","title":"Bigger, Regularized, Categorical: High-Capacity Value Functions are Efficient Multi-Task Learners","created":"2025-05-29","cited_by_count":23,"area":null},{"id":"2506.08902","title":"Intention-Conditioned Flow Occupancy Models","created":"2025-06-10","cited_by_count":9,"area":null},{"id":"2605.03065","title":"OGPO: Sample Efficient Full-Finetuning of Generative Control Policies","created":"2026-05-04","cited_by_count":1,"area":null},{"id":"2607.03723","title":"OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies","created":"2026-07-04","cited_by_count":1,"area":null},{"id":"2506.01568","title":"Trajectory First: A Curriculum for Discovering Diverse Policies","created":"2025-06-02","cited_by_count":0,"area":null},{"id":"2606.25527","title":"Beyond One-Size-Fits-All: Diagnosis-Driven Online Reinforcement Learning with Offline Priors","created":"2026-06-24","cited_by_count":0,"area":null},{"id":"2610.20607","title":"Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}