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English(EN) Diffusion Policy Improvement with Proposal-Conditioned Refinement Flows

新研究改进用于强化学习和离散采样的扩散模型

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新研究论文介绍了改进扩散模型的新颖方法。第一篇PReFlow通过结合基于评判器的提案选择和条件细化流来增强离线强化学习，在OGBench任务上取得了有竞争力的性能。第二篇FluxLite为离散扩散模型提供了一个无需训练的框架，该框架可以控制推理时的提案，显著降低了KL散度，并提高了2D Ising模型等基准测试的采样精度。 AI

影响 这些论文介绍了提高扩散模型效率和准确性的新颖技术，可能对强化学习和生成采样等领域产生影响。

排序理由 两篇在arXiv上发表的学术论文，详细介绍了扩散模型的新方法。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新研究改进用于强化学习和离散采样的扩散模型

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两篇在arXiv上发表的学术论文，详细介绍了扩散模型的新方法。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Junhyun Ha, Juho Lee, Byungwoo Park ·

    基于提案条件细化流的扩散策略改进

    arXiv:2609.36812v1 Announce Type: cross Abstract: Diffusion and flow policies can model complex behaviors in offline reinforcement learning (RL). However, penalizing their KL divergence from the behavior policy can discourage actions having high critic values with low behavior de…

  2. arXiv cs.LG TIER_1 English(EN) · Yinuo Ren, Haoxuan Chen, Grant M. Rotskoff, Jiequn Han, Lexing Ying ·

    FluxLite：离散扩散模型的推理时提议控制

    arXiv:2609.35947v1 Announce Type: new Abstract: Many inference-time tasks for pretrained discrete diffusion models and diffusion language models reduce to drawing samples from a tilted version of the pretrained distribution. Feynman-Kac sequential Monte Carlo (SMC) makes this cor…

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