arXiv AI

TrustRoboReward: Preference-Ordered Isotonic Score Editing for Multi-Paradigm Robot Reward Models

arXiv:2608. 08491v1 Announce Type: new Abstract: Reward models are a bottleneck for reinforcement learning in embodied AI.

arXiv AI

Jul 24

TOPReward: Token Probabilities as Hidden Zero-Shot Rewards for Robotics

arXiv:2602. 19313v2 Announce Type: replace-cross Abstract: General-purpose robot learning requires dense, instruction-conditioned feedback that can distinguish meaningful task progress from stalled, failed, or partially completed behavior.

By Shirui Chen, Cole Harrison, Ying-Chun Lee, Angela Jin Yang, Zhongzheng Ren, Lillian J. Ratliff, Jiafei Duan, Dieter Fox, Ranjay Krishna

llms robotics multimodal benchmarks

arXiv Machine Learning

Aug 11

RynnValue: Scaling Robotic Value Foundation Models with Temporal Distance

arXiv:2608. 09853v1 Announce Type: cross Abstract: General-purpose reward models are increasingly the bottleneck for scaling robot learning, yet the recipe for learning value-related capabilities from large-scale heterogeneous corpora remains underexplored.

By Dongchi Huang, Hongyin Zhang, Bohan Hou, Siteng Huang, Zhian Su, Hang Guo, Tong Lu, Zhaofeng Xu, Jiahao Tang, Jianfei Yang, Donglin Wang, Peixi Peng, Mingxiu Chen, Deli Zhao, Xin Li

robotics

arXiv AI

Aug 7

OSReward: Instituting Standardized Evaluation for Cross-Platform Computer-Use Reward Models

arXiv:2607. 28609v2 Announce Type: replace Abstract: Computer-using agents (CUAs) are advancing rapidly across the digital world.

By Qiushi Sun, Kanzhi Cheng, Yian Wang, Bowen Yang, Hang Yan, Liheng Chen, Fangzhi Xu, Zichen Ding, Nuo Chen, Jialin Cao, Xingdong Gong, Zehao Li, Kaiming Jin, Xinfeng Yuan, Zhoumianze Liu, Jingyang Gong, Zhangyue Yin, Jiahui Gao, Zhiyong Wu, Tianbao Xie, Jianbing Zhang, Ben Kao, Lingpeng Kong

llms agents reinforcement-learning multimodal benchmarks safety

arXiv Machine Learning

Jun 29

Qwen-Image-2.0-RL Technical Report

arXiv:2606. 27608v1 Announce Type: cross Abstract: We present Qwen-Image-2.

By Yixian Xu, Kaiyuan Gao, Yuxiang Chen, Yilei Chen, Zecheng Tang, Zihao Liu, Zikai Zhou, Deqing Li, Hao Meng, Kuan Cao, Jiahao Li, Jie Zhang, Liang Peng, Lihan Jiang, Ningyuan Tang, Shengming Yin, Tianhe Wu, Xiaoyue Chen, Yan Shu, Yanran Zhang, Yi Wang, Yu Wu, Yujia Wu, Zekai Zhang, Zhendong Wang, Xiao Xu, Kun Yan, Chenfei Wu

llms diffusion reinforcement-learning fine-tuning efficiency multimodal safety

arXiv Machine Learning

23h ago

Prism-GRPO: Faster VLA Policy Optimization via Splitting Same-outcome Groups

arXiv:2608. 17423v1 Announce Type: cross Abstract: GRPO is increasingly used for reinforcement learning of vision-language-action (VLA) policies because, unlike PPO, it does not require training a critic.

By Zeyun Deng, Yuzhe Lu, Yawei Wang, Linbo Liu, Qing Ping, Han Ding, Guande Wu, Panpan Xu, Jun Huan

reinforcement-learning robotics multimodal safety

arXiv Machine Learning

Jun 5

Reward Learning through Ranking Mean Squared Error

arXiv:2601. 09236v3 Announce Type: replace Abstract: Reward design remains a significant bottleneck in applying reinforcement learning (RL) to real-world problems.

By Chaitanya Kharyal, Calarina Muslimani, Matthew E. Taylor

reinforcement-learning robotics benchmarks

arXiv AI

Jul 3

Rank-Then-Act: Reward-Free Control from Frame-Order Progress

arXiv:2607. 01897v1 Announce Type: cross Abstract: We introduce Rank-Then-Act (RTA), a framework for learning control policies from expert video demonstrations without environment rewards.

By Yuriy Maksyuta, George Bredis, Ruslan Rakhimov, Daniil Gavrilov

llms reinforcement-learning multimodal benchmarks

arXiv Machine Learning

Jun 18

The Reward Was in Your Data All Along: Correcting Flow Matching with Discriminator-Guided RL

arXiv:2606. 19162v1 Announce Type: new Abstract: Score- and flow-matching models often rely on preference-based reinforcement learning for two purposes: aligning with subjective preferences and, surprisingly, recovering properties such as visual realism and coherent object structure that matching-based training is intended to learn from the data itself.

By Nicolas Beltran-Velez, Felix Friedrich, Zhang Xiaofeng, Reyhane Askari-Hemmat, Xiaochuang Han, Adriana Romero-Soriano, Michal Drozdzal

diffusion reinforcement-learning safety

arXiv Machine Learning

Aug 5

Crayotter: Learning Long-Horizon Video Editing Agents via Group-Relative Preference Backpropagation

arXiv:2608. 02694v1 Announce Type: cross Abstract: Long-horizon video editing agents receive final-product feedback only after many interdependent decisions.

By Lecheng Yan, Jianze Lin, Yichong Zhang, Ben Pan, Wenxi Li, Chenyang Lyu, Liting Zhou, Cathal Gurrin

agents benchmarks

arXiv AI

Jun 26

Automating Potential-based Reward Shaping with Vision Language Model Guidance

arXiv:2606. 27180v1 Announce Type: cross Abstract: Sparse rewards are inherently challenging for reinforcement learning agents as they lack intermediate feedback to guide exploration and to correctly attribute the sparse success rewards to relevant parts of the trajectory.

By Henrik M\"uller, Daniel Kudenko

llms agents reinforcement-learning multimodal

arXiv AI

1d ago

Robo-Dopamine 2.0: History-Conditioned and OOD-Aware Process Reward Modeling for Robotic Manipulation

arXiv:2608. 15680v1 Announce Type: cross Abstract: Vision-language-action (VLA) models improve robotic manipulation but remain vulnerable to compounding errors, scene changes, and off-trajectory states.

By Yijie Xu, Haopeng Jin, Run Zhou, Shengbang Liu, Sixiang Chen, Hongyang Cheng, Sicheng Hu, Peterson Co, Jinwen Luo, Huajie Tan, Shanghang Zhang

reinforcement-learning robotics multimodal benchmarks

arXiv AI

Jun 30

RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis

arXiv:2606. 28385v1 Announce Type: cross Abstract: Recent advances in robot world models enable synthetic video generation for embodied prediction and planning.

By Minh-Loi Nguyen, Nghiem Tuong Diep, Hung Khang Nguyen, Minh Le, Doanh Le Thien, Hoang H. Tran, Dung D. Le, Vu N. Duong, Daniel Sonntag, An Thai Le, Duy Minh Ho Nguyen, Vien Anh Ngo, Tran Van Nhiem

llms agents diffusion robotics multimodal benchmarks safety

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