Composite Flow Matching for Reinforcement Learning with Shifted-Dynamics Data
NeurIPSSpotlight2025
TL;DR
Incorporating pre-collected offline data can substantially improve the sample efficiency of reinforcement learning (RL), but its benefits can break down when the transition dynamics in the offline dat…
Opening excerpt from the authors’ abstract. source
Read the paper
Topics
reinforcement learning flow matching
← All NeurIPS 2025 Spotlight papers · Browse the whole archive