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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 for Preference-based Reinforcement Learning
Chenyang Cao, Miguel Rogel-GarcÃa, Mohamed Nabail +2
Preference-based Reinforcement Learning (PbRL) provides a way to learn high-performance policies in environments where the reward signal is hard to specify, avoiding heuristic and…