Composite Flow Matching for Reinforcement Learning with Shifted-Dynamics Data

NeurIPSSpotlight2025

Authors
Lingkai Kong, Haichuan Wang, Tonghan Wang, GUOJUN XIONG, Milind Tambe
Venue
NeurIPS 2025
Track
Spotlight

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