Guided Flow Policy: Learning from High-Value Actions in Offline Reinforcement Learning
Franki NGUIMATSIA TIOFACK, Théotime Le Hellard, Fabian Schramm, Nicolas Perrin-Gilbert, Justin Carpentier
Abstract
Offline reinforcement learning often relies on behavior regularization that enforces policies to remain close to the dataset distribution. However, such approaches fail to distinguish between high-value and low-value actions in their regularization components. We introduce Guided Flow Policy (GFP), which couples a multi-step flow-matching policy with a distilled one-step actor. The actor directs the flow policy through weighted behavior cloning to focus on cloning high-value actions from the dataset rather than indiscriminately imitating all state-action pairs. In turn, the flow policy constrains the actor to remain aligned with the dataset's best transitions while maximizing the critic. This mutual guidance enables GFP to achieve state-of-the-art performance across 144 state and pixel-based tasks from the OGBench, Minari, and D4RL benchmarks, with substantial gains on suboptimal datasets and challenging tasks.
BibTeX
@inproceedings{
tiofack2026guided,
title={Guided Flow Policy: Learning from High-Value Actions in Offline Reinforcement Learning},
author={Franki NGUIMATSIA TIOFACK and Th{\'e}otime Le Hellard and Fabian Schramm and Nicolas Perrin-Gilbert and Justin Carpentier},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=EBjy1rmpv0}
}