JEPA · 2026-10-16
Reinforcement Learning-driven Diffusion Models: Language, Vision, and Beyond
Zhiyuan Ma, Jiabao Wei, Tianyu Wang, Hanqing Wang, Jiadong Pan, Haowen Xu, Liangliang Zhao, Ruixun Liu, Yuhan Wang, Yuwei Zhang, Liang Feng, Junhao Xiao, Kaiyan Zhang, Xuekai Zhu, Daizong Liu, Biqing Qi, Yihao Liu, Ganqu Cui, Ning Ding, Linfeng Zhang, Jianjun Li, Kun He, Xiang Bai, Bowen Zhou
AI-assisted summary based on the paper. Consult the linked source for methods and limitations. Benchmark numbers appear after editor verification.
TL;DR
The authors review and analyze “RL-driven diffusion models” where reinforcement learning post-trains diffusion models by reward-driven trajectory optimization. They discuss preference- and policy-based RL formulations for diffusion, survey RL for diffusion-based language, multimodal, robotic, and scientific models, and identify three bottlenecks for real-world deployment: reward design fragility, exploration–exploitation trade-off, and sparse-reward training instability. They also provide a public repository of related work.
Why it matters
Diffusion models are strong at high-fidelity controllable generation but struggle with dynamic multi-step decision problems and alignment with human preferences or task objectives. By framing denoising steps as an RL “thinking” trajectory, RL-driven diffusion aims to optimize generation with explicit or verifiable rewards. The paper’s roadmap and bottleneck analysis target why RL integration is still challenging and how to make RL-diffusion more robust for downstream tasks.
Method
- Formulate diffusion denoising as an RL Markov decision process: each denoising step is an action, rewards can be terminal-only under RLVR, and policy optimization uses PPO or GRPO.
- Preference-based RL for diffusion via RLHF (reward model trained from preference triples) and DPO (direct alignment from pairwise preference data without an explicit reward model).
- Organize RL-for-diffusion approaches along trajectory level (token/phrase/sequence credit assignment) and reward density (terminal-only, dense process rewards, and related variants).
Limitation
The authors identify three bottlenecks that currently hinder real-world deployment of RL-driven DMs: reward design fragility, the exploration-exploitation trade-off in high-dimensional generation spaces, and training instability under sparse rewards.
Abstract (from arXiv)
Diffusion models (DMs) have steadily demonstrated outstanding performance in high-fidelity, controllable generation across diverse scenarios, following the "pretraining-finetuning" paradigm established in LLMs. Despite these advances, conventional DMs still face critical limitations in sophisticated reasoning, autonomous exploration of generation pathways, and adaptability to complex, human-aligned objectives. Inspired by RLHF's success in LLMs, RL has recently emerged as a pivotal post-training mechanism to iteratively refine DMs through reward-driven trajectory optimization. This paper presents the first comprehensive review of RL-driven DMs and an empirical analysis of how RL reshapes the diffusion process towards experience-centric, goal-directed generation. We propose a novel technical panorama elucidating RL's multifaceted roles in diffusion-based language, multimodal, robotic, and scientific models, with a granular focus on diffusion trajectory optimization and reward engineering. Through extensive experiments on multiple task-specific benchmarks, we systematically evaluate representative methods to intuitively highlight the advantages of different RL post-training strategies. Furthermore, we reveal and dissect three crucial bottlenecks that currently hinder the real-world deployment of RL-driven DMs: reward design fragility, exploration-exploitation trade-off in high-dimensional generation spaces, and training instability under sparse rewards. Based on these perspectives, we provide a principled roadmap for the integration of RL into DMs and outline promising future research directions for robust, interpretable RL-diffusion systems. We also construct a public repository to continuously track the latest developments in this rapidly evolving field: https://github.com/MAIR-Lab-HUST/Awesome-RL-for-Diffusion-Models.
Related papers
- CFG-Policy: Unified Diffusion Policy Learning Framework via Classifier-free Guidance
- DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models
- FARL: A Fully Automated Pipeline of RL Interface and Solver Design
- Progress Reward Model for Reinforcement Learning via Large Language Models
- Reachability-Aware Diffusion Policy Optimization
Publication record
Bibliographic evidence describes where and when a record appeared. It does not validate scientific claims.
First public release: 2026-10-15 · Indexed by Pub-AI: 2026-10-16 UTC
Reinforcement Learning-driven Diffusion Models: Language, Vision, and Beyond
Preprint posted: 2026-10-15 · Original record ↗
- arxiv
- 2610.21539
Explicit source versions (1)
- v1 · 2026-10-15
- Preprint available
Source date: 2026-10-15 · First detected: 2026-10-25 UTC · Historical baseline
Source evidence (1)
- Official evidence ↗ · atom:published (official metadata)
Abstract · arxiv
Diffusion models (DMs) have steadily demonstrated outstanding performance in high-fidelity, controllable generation across diverse scenarios, following the "pretraining-finetuning" paradigm established in LLMs. Despite these advances, conventional DMs still face critical limitations in sophisticated reasoning, autonomous exploration of generation pathways, and adaptability to complex, human-aligned objectives. Inspired by RLHF's success in LLMs, RL has recently emerged as a pivotal post-training mechanism to iteratively refine DMs through reward-driven trajectory optimization. This paper presents the first comprehensive review of RL-driven DMs and an empirical analysis of how RL reshapes the diffusion process towards experience-centric, goal-directed generation. We propose a novel technical panorama elucidating RL's multifaceted roles in diffusion-based language, multimodal, robotic, and scientific models, with a granular focus on diffusion trajectory optimization and reward engineering. Through extensive experiments on multiple task-specific benchmarks, we systematically evaluate representative methods to intuitively highlight the advantages of different RL post-training strategies. Furthermore, we reveal and dissect three crucial bottlenecks that currently hinder the real-world deployment of RL-driven DMs: reward design fragility, exploration-exploitation trade-off in high-dimensional generation spaces, and training instability under sparse rewards. Based on these perspectives, we provide a principled roadmap for the integration of RL into DMs and outline promising future research directions for robust, interpretable RL-diffusion systems. We also construct a public repository to continuously track the latest developments in this rapidly evolving field: https://github.com/MAIR-Lab-HUST/Awesome-RL-for-Diffusion-Models.