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Evidence record 1292 · automatically gathered

Reinforcement Learning for Flow-Matching Policies with Density Transport

We present an online reinforcement learning (RL) algorithm for fine-tuning flow-matching policies in continuous-control problems. Our key insight is to view RL-based policy improvement as a transport of action densities towards regions of high reward, which naturally aligns with the transport formulation of flow matching models. Prior methods either approximate the current or optimal policy distribution or resort to distillation, which introduces biased gradients or sacrifices multimodal modelin

Record details

Published: 7 June 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Regulation
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (7 June 2026), “Reinforcement Learning for Flow-Matching Policies with Density Transport,” evidence record 1292, https://ethics.ai/record/1292 (originally published by arXiv).

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