arXiv Machine Learning

Amortizing intractable inference in diffusion models for vision, language, and control

The paper introduces a data‑free learning objective called relative trajectory balance for training diffusion models to sample from a posterior defined by a diffusion prior and an arbitrary black‑box constraint or likelihood. It proves asymptotic correctness of this objective and demonstrates its use across vision, language, and multimodal tasks, including classifier guidance, language infilling, and text‑to‑image generation. Additionally, the method is applied to continuous control with a score‑based behavior prior, achieving state‑of‑the‑art results in offline reinforcement learning.

arXiv Machine Learning

Aug 31

Improved off-policy training of diffusion samplers

The paper investigates training diffusion models to sample from distributions defined by unnormalized densities or energy functions. It benchmarks various diffusion-structured inference techniques, including simulation-based variational methods and off-policy approaches such as continuous generative flow networks, highlighting their relative strengths and challenging some prior claims. Additionally, the authors introduce a new exploration strategy for off-policy methods that employs local search in the target space with a replay buffer, demonstrating improved sample quality across multiple target distributions.

By Marcin Sendera, Minsu Kim, Sarthak Mittal, Pablo Lemos, Luca Scimeca, Jarrid Rector-Brooks, Alexandre Adam, Yoshua Bengio, Esmeralda S. Whitammer

diffusion benchmarks

arXiv AI

Jun 16

Sample from What You See: Visuomotor Policy Learning via Diffusion Bridge with Observation-Embedded Stochastic Differential Equation

arXiv:2512. 07212v3 Announce Type: replace Abstract: Imitation learning with diffusion models has advanced robotic control by capturing the multi-modal action distributions.

By Zhaoyang Liu, Mokai Pan, Zhongyi Wang, Kaizhen Zhu, Haotao Lu, Haipeng Zhang, Jingya Wang, Ye Shi

diffusion robotics multimodal benchmarks

arXiv Machine Learning

Sep 22

D-IMPL: A Diffusion-based Solver for Parameterized BBOs

arXiv:2609. 22752v1 Announce Type: new Abstract: Diffusion models have demonstrated strong power in generative modeling tasks across multiple domains, exhibiting a remarkable capability of learning complex distributions from samples.

By Yang Hu, Na Li

diffusion multimodal

arXiv Machine Learning

Sep 1

Data-to-Energy Stochastic Dynamics

arXiv:2509.26364v3 Announce Type: replace Abstract: The Schr\"odinger bridge problem is concerned with finding a stochastic dynamical system bridging two marginal distributions that minimises a certa...

By Kirill Tamogashev, Esmeralda S. Whitammer

diffusion reinforcement-learning multimodal

arXiv Machine Learning

Jun 11

Reverse Flow Matching: A Unified Framework for Online Reinforcement Learning with Diffusion and Flow Policies

arXiv:2601. 08136v2 Announce Type: replace Abstract: Diffusion and flow policies are gaining prominence in online reinforcement learning (RL) due to their expressive power, yet training them efficiently remains a critical challenge.

By Zeyang Li, Sunbochen Tang, Navid Azizan

diffusion reinforcement-learning benchmarks

arXiv Machine Learning

Aug 5

Information-Geometric Forward Policy Training in GFlowNets

arXiv:2608. 03967v1 Announce Type: cross Abstract: Generative Flow Networks (GFlowNets) have emerged as a flexible framework for amortised inference over discrete and mixed discrete-continuous objects, requiring only an unnormalised target density specified through a reward.

By Yordan Raykov, Rodrigo Veiga

arXiv Machine Learning

4d ago

Generative Actor-Critic with Soft Bridge Policies

arXiv:2605.08733v2 Announce Type: replace Abstract: Expressive generative policies such as diffusion and flow models are appealing for maximum-entropy (MaxEnt) online reinforcement learning because o...

By Ke He, Le He, Shunpu Tang, Yafei Wang, Lisheng Fan

diffusion reinforcement-learning multimodal benchmarks

arXiv AI

Jun 11

Diffusing to Coordinate: Efficient Online Multi-Agent Diffusion Policies

arXiv:2602. 18291v2 Announce Type: replace Abstract: Online Multi-Agent Reinforcement Learning (MARL) is a prominent framework for efficient agent coordination.

By Zhuoran Li, Hai Zhong, Xun Wang, Qingxin Xia, Lihua Zhang, Longbo Huang

agents diffusion reinforcement-learning multimodal benchmarks

arXiv AI

Sep 15

Learning Multimodal One-step Flow Policy via Value-weighted Optimal Transport

arXiv:2609.15883v1 Announce Type: cross Abstract: Offline reinforcement learning aims to learn a policy solely from fixed datasets, which often contain multimodal action distributions. Flow policies...

By Jaehun Shon, Jinha Choi, Jongwook Jeon, Jongmin Lee

reinforcement-learning efficiency multimodal benchmarks safety

arXiv Machine Learning

Aug 26

Scaling Reinforcement Learning for Diffusion Models via Velocity Matching

arXiv:2608.23664v1 Announce Type: cross Abstract: Reward fine-tuning is becoming an important tool for adapting diffusion models to human preferences and task-specific objectives, but existing method...

By Jaemoo Choi, Wei Guo, Yuchen Zhu, Arash Vahdat, Molei Tao, Julius Berner, Yongxin Chen

llms diffusion reinforcement-learning fine-tuning

arXiv Machine Learning

Jul 20

Dichotomous Diffusion Policy Optimization

arXiv:2601. 00898v3 Announce Type: replace Abstract: Diffusion-based policies have gained growing popularity in solving a wide range of decision-making tasks due to their superior expressiveness and controllable generation during inference.

By Ruiming Liang, Yinan Zheng, Kexin Zheng, Tianyi Tan, Jianxiong Li, Liyuan Mao, Zhihao Wang, Guang Chen, Hangjun Ye, Jingjing Liu, Jinqiao Wang, Xianyuan Zhan

agents diffusion reinforcement-learning robotics multimodal benchmarks

arXiv Machine Learning

4d ago

Asymptotically Exact Negative Guidance of Diffusion Models via Positive-Unlabeled Learning

arXiv:2610.19802v1 Announce Type: new Abstract: Negative guidance of diffusion models generates samples that avoid an undesired condition, specified either by reference samples or by a conditional sc...

By Hirohane Takagi, Masashi Sugiyama

diffusion safety

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