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This is a Experimental Study study.

October 20, 2025Open Access

Unleashing Flow Policies with Distributional Critics

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Authors

DCDeshu Chen YLYuchen Liu ZZZhijian Zhou

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Overview

This research demonstrates Distributional Flow Critic's impact on multimodal action distributions in reinforcement learning, suggesting improved stability in learning signals.

Key Points

Distributional Flow Critic enhances flow-based policies by providing a more informative learning signal.
Extensive experiments show that DFC significantly improves performance on challenging tasks requiring multimodal action distributions.
By modeling the complete state-action return distribution, DFC overcomes limitations of traditional critics in reinforcement learning.
The proposed method excels in both offline and offline-to-online fine-tuning, outperforming existing approaches.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68f6196ee0bbbc94fac364f4 https://doi.org/10.48550/arxiv.2509.23087

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October 20, 2025Open Access

Unleashing Flow Policies with Distributional Critics

View Full Paper

Ask AI

Bookmark

Authors

DCDeshu Chen YLYuchen Liu ZZZhijian Zhou

Discussion

Member takes

Overview

This research demonstrates Distributional Flow Critic's impact on multimodal action distributions in reinforcement learning, suggesting improved stability in learning signals.

Key Points

Distributional Flow Critic enhances flow-based policies by providing a more informative learning signal.
Extensive experiments show that DFC significantly improves performance on challenging tasks requiring multimodal action distributions.
By modeling the complete state-action return distribution, DFC overcomes limitations of traditional critics in reinforcement learning.
The proposed method excels in both offline and offline-to-online fine-tuning, outperforming existing approaches.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68f6196ee0bbbc94fac364f4 https://doi.org/10.48550/arxiv.2509.23087

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