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Vision-Language-Action (VLA) models have recently emerged as a powerful paradigm for robotic manipulation.
Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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Inverse reward design
Dylan Hadfield-Menell, Smitha Milli, Pieter Abbeel, Stuart J Russell, and Anca Dragan · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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What matters in learning from offline human demonstrations for robot manipulation
Ajay Mandlekar, Danfei Xu, Josiah Wong, Soroush Nasiriany, Chen Wang, Rohun Kulkarni, Li Fei-Fei, Silvio Savarese, Yuke Zhu, and Roberto Martín-Martín · 2021
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How to train your robot with deep reinforcement learning: lessons we have learned
Julian Ibarz, Jie Tan, Chelsea Finn, Mrinal Kalakrishnan, Peter Pastor, and Sergey Levine · 2021
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A review of robot learning for manipulation: Challenges, representations, and algorithms
Oliver Kroemer, Scott Niekum, and George Konidaris · 2021
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A survey on text generation using generative adversarial networks
Gustavo H De Rosa and Joao P Papa · 2021
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Learning dense rewards for contact-rich manipulation tasks
Zheng Wu, Wenzhao Lian, Vaibhav Unhelkar, Masayoshi Tomizuka, and Stefan Schaal · 2021
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Training compute-optimal large language models
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Vip: Towards universal visual reward and representation via value-implicit pre-training
Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman, Osbert Bastani, Vikash Kumar, and Amy Zhang · 2022
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Eureka: Human-level reward design via coding large language models
Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan, and Anima Anandkumar · 2023
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Reward (mis) design for autonomous driving
W Bradley Knox, Alessandro Allievi, Holger Banzhaf, Felix Schmitt, and Peter Stone · 2023
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The perils of trial-and-error reward design: misdesign through overfitting and invalid task specifications
Serena Booth, W Bradley Knox, Julie Shah, Scott Niekum, Peter Stone, and Alessandro Allievi · 2023
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Libero: Benchmarking knowledge transfer for lifelong robot learning
Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, and Peter Stone · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2023
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Openvla: An open-source vision-language-action model
Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Grace Lam, Pannag Sanketi, et al · 2024
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Open x-embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration 0
Abby O’Neill, Abdul Rehman, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, et al · 2024
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Efficient data collection for robotic manipulation via compositional generalization
Jensen Gao, Annie Xie, Ted Xiao, Chelsea Finn, and Dorsa Sadigh · 2024
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Hybridflow: A flexible and efficient rlhf framework
Guangming Sheng, Chi Zhang, Zilingfeng Ye, Xibin Wu, Wang Zhang, Ru Zhang, Yanghua Peng, Haibin Lin, and Chuan Wu · 2024
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Deepseekmath: Pushing the limits of mathematical reasoning in open language models
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al · 2024
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The effect of sampling temperature on problem solving in large language models
Matthew Renze · 2024
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pi_0: A vision-language-action flow model for general robot control
Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, et al · 2024
Simplerl-zoo: Investigating and taming zero reinforcement learning for open base models in the wild
Weihao Zeng, Yuzhen Huang, Qian Liu, Wei Liu, Keqing He, Zejun Ma, and Junxian He · 2025
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Fine-tuning vision-language-action models: Optimizing speed and success
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Dapo: An open-source llm reinforcement learning system at scale
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Rdt-1b: a diffusion foundation model for bimanual manipulation
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Octo: An open-source generalist robot policy
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Diffusion policy: Visuomotor policy learning via action diffusion
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Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, et al · 2024
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Learning to manipulate anywhere: A visual generalizable framework for reinforcement learning
Zhecheng Yuan, Tianming Wei, Shuiqi Cheng, Gu Zhang, Yuanpei Chen, and Huazhe Xu · 2024
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Robotic control via embodied chain-of-thought reasoning
Michał Zawalski, William Chen, Karl Pertsch, Oier Mees, Chelsea Finn, and Sergey Levine · 2024
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Qiying Yu, Zheng Zhang, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, Yu Yue, Tiantian Fan, Gaohong Liu, Lingjun Liu, Xin Liu, et al · 2025
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Polaris: A post-training recipe for scaling reinforcement learning on advanced reasoning models, 2025
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Efficient reinforcement finetuning via adaptive curriculum learning
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Enhancing efficiency and exploration in reinforcement learning for llms
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Robotwin: Dual-arm robot benchmark with generative digital twins (early version)
Yao Mu, Tianxing Chen, Shijia Peng, Zanxin Chen, Zeyu Gao, Yude Zou, Lunkai Lin, Zhiqiang Xie, and Ping Luo · 2025
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Fast: Efficient action tokenization for vision-language-action models
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Nora: A small open-sourced generalist vision language action model for embodied tasks
Chia-Yu Hung, Qi Sun, Pengfei Hong, Amir Zadeh, Chuan Li, U Tan, Navonil Majumder, Soujanya Poria, et al · 2025
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pi_ { \{ 0.5 } \} : a vision-language-action model with open-world generalization
Physical Intelligence, Kevin Black, Noah Brown, James Darpinian, Karan Dhabalia, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, et al · 2025
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H-rdt: Human manipulation enhanced bimanual robotic manipulation
Hongzhe Bi, Lingxuan Wu, Tianwei Lin, Hengkai Tan, Zhizhong Su, Hang Su, and Jun Zhu · 2025
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Ttrl: Test-time reinforcement learning
Yuxin Zuo, Kaiyan Zhang, Li Sheng, Shang Qu, Ganqu Cui, Xuekai Zhu, Haozhan Li, Yuchen Zhang, Xinwei Long, Ermo Hua, et al · 2025
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Haoran Geng, Feishi Wang, Songlin Wei, Yuyang Li, Bangjun Wang, Boshi An, Charlie Tianyue Cheng, Haozhe Lou, Peihao Li, Yen-Jen Wang, et al · 2025
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Reinbot: Amplifying robot visual-language manipulation with reinforcement learning
Hongyin Zhang, Zifeng Zhuang, Han Zhao, Pengxiang Ding, Hongchao Lu, and Donglin Wang · 2025
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Interactive post-training for vision-language-action models
Shuhan Tan, Kairan Dou, Yue Zhao, and Philipp Krähenbühl · 2025
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Rlinf: Reinforcement learning infrastructure for agentic ai
RLinf Team · 2025
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Vla-rl: Towards masterful and general robotic manipulation with scalable reinforcement learning
Guanxing Lu, Wenkai Guo, Chubin Zhang, Yuheng Zhou, Haonan Jiang, Zifeng Gao, Yansong Tang, and Ziwei Wang · 2025
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Rftf: Reinforcement fine-tuning for embodied agents with temporal feedback
Junyang Shu, Zhiwei Lin, and Yongtao Wang · 2025
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