Guided Flow Policy: Learning from High-Value Actions in Offline Reinforcement Learning

• URL: http://arxiv.org/abs/2512.03973v1
• Date: Wed, 03 Dec 2025 17:05:58 GMT
• Title: Guided Flow Policy: Learning from High-Value Actions in Offline Reinforcement Learning
• Authors: Franki Nguimatsia Tiofack, Théotime Le Hellard, Fabian Schramm, Nicolas Perrin-Gilbert, Justin Carpentier,
• Abstract summary: We introduce Guided Flow Policy, which couples a multi-step flow-matching policy with a distilled one-step actor.<n>The actor directs the flow policy through weighted behavior cloning to focus on cloning high-value actions from the dataset.<n>This mutual guidance enables GFP to achieve state-of-the-art performance across 144 state and pixel-based tasks.
• Score: 10.037416068775853
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. Webpage: https://simple-robotics.github.io/publications/guided-flow-policy/

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