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cs.LG2026
World-Model Policy Arbiter for Goal-Conditioned Reinforcement Learning
Junwei Quan, Evgenii Opryshko, Nicholas Rhinehart +1
Offline goal-conditioned reinforcement learning (GCRL) has produced a diverse set of goal-reaching algorithms, yet no single algorithm performs best across environments, goals, and…
cs.LG2026
QPILOTS: Efficient Test-Time Q-Steering for Flow Policies
Yifan Ruan, Chenyang Cao, Andreas Burger +7
Flow-matching and diffusion policies are expressive action generators, but optimizing them with temporal-difference reinforcement learning (RL) remains difficult. Effective policy…
cs.LG2025
Residual Reward Models: Leveraging Prior Knowledge for Efficient Preference-based Reinforcement Learning in Robotics
Chenyang Cao, Miguel Rogel-García, Mohamed Nabail +2
Preference-based Reinforcement Learning (PbRL) provides a promising alternative to heuristic reward design in complex robotic environments. However, PbRL often suffers from poor sa…