Reinforcement Learning in Vision: A Survey
Recent advances at the intersection of reinforcement learning (RL) and visual
intelligence have enabled agents that not only perceive complex visual scenes
but also reason, generate, and act within them. This survey offers a critical
and up-to-date synthesis of the field. We first formalize visual RL problems
and trace the evolution of policy-optimization strategies from RLHF to
verifiable reward paradigms, and from Proximal Policy Optimization to Group
Relative Policy Optimization. We then organize more than 200 representative
works into four thematic pillars: multi-modal large language models, visual
generation, unified model frameworks, and vision-language-action models. For
each pillar we examine algorithmic design, reward engineering, benchmark
progress, and we distill trends such as curriculum-driven training,
preference-aligned diffusion, and unified reward modeling. Finally, we review
evaluation protocols spanning set-level fidelity, sample-level preference, and
state-level stability, and we identify open challenges that include sample
efficiency, generalization, and safe deployment. Our goal is to provide
researchers and practitioners with a coherent map of the rapidly expanding
landscape of visual RL and to highlight promising directions for future
inquiry. Resources are available at:
https://github.com/weijiawu/Awesome-Visual-Reinforcement-Learning.
689ad956fab6fdd2e52ac54c
Weijia Wu
689ad956fab6fdd2e52ac54d
Chen Gao
689ad956fab6fdd2e52ac54e
Joya Chen
689ad956fab6fdd2e52ac54f
Kevin Qinghong Lin
claimed_verified
2025-08-13T07:19:03.159Z
64440be5af034cdfd69ca3a7
https://cdn-avatars.huggingface.co/v1/production/uploads/64440be5af034cdfd69ca3a7/qmx24QiDFT29vleCxL9TX.jpeg
Qinghong (Kevin) Lin
user
KevinQHLin
689ad956fab6fdd2e52ac550
Qingwei Meng
689ad956fab6fdd2e52ac551
Yiming Zhang
689ad956fab6fdd2e52ac552
Yuke Qiu
689ad956fab6fdd2e52ac553
Hong Zhou
689ad956fab6fdd2e52ac554
Mike Zheng Shou
Reinforcement Learning in Vision: A Survey
Recent advances at the intersection of reinforcement learning (RL) and visual
intelligence have enabled agents that not only perceive complex visual scenes
but also reason, generate, and act within them. This survey offers a critical
and up-to-date synthesis of the field. We first formalize visual RL problems
and trace the evolution of policy-optimization strategies from RLHF to
verifiable reward paradigms, and from Proximal Policy Optimization to Group
Relative Policy Optimization. We then organize more than 200 representative
works into four thematic pillars: multi-modal large language models, visual
generation, unified model frameworks, and vision-language-action models. For
each pillar we examine algorithmic design, reward engineering, benchmark
progress, and we distill trends such as curriculum-driven training,
preference-aligned diffusion, and unified reward modeling. Finally, we review
evaluation protocols spanning set-level fidelity, sample-level preference, and
state-level stability, and we identify open challenges that include sample
efficiency, generalization, and safe deployment. Our goal is to provide
researchers and practitioners with a coherent map of the rapidly expanding
landscape of visual RL and to highlight promising directions for future
inquiry. Resources are available at:
https://github.com/weijiawu/Awesome-Visual-Reinforcement-Learning.
This survey synthesizes recent advancements in visual reinforcement learning, covering policy optimization strategies, thematic pillars, and evaluation protocols, while highlighting open challenges.
reinforcement learning visual intelligence visual RL policy-optimization strategies RLHF verifiable reward paradigms Proximal Policy Optimization Group Relative Policy Optimization multi-modal large language models visual generation unified model frameworks vision-language-action models curriculum-driven training preference-aligned diffusion unified reward modeling set-level fidelity sample-level preference state-level stability sample efficiency generalization safe deployment