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Neural scaling laws have driven significant advancements in machine learning, particularly in domains like language modeling and computer vision.
An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
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Deep learning scaling is predictable, empirically
Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md Mostofa Ali Patwary, Yang Yang, and Yanqi Zhou · 2017
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The economic impact of moore’s law: Evidence from when it faltered
Neil Thompson · 2017
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A Vaswani · 2017
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AI and compute , 2018
Dario Amodei and Danny Hernandez · 2018
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A constructive prediction of the generalization error across scales
Jonathan S Rosenfeld, Amir Rosenfeld, Yonatan Belinkov, and Nir Shavit · 2019
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On the relation between neural network size and performance
Jonathan Shmuel Rosenfeld · 2019
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The bitter lesson
Richard Sutton · 2019
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Scaling laws for autoregressive generative modeling
Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, Tom B Brown, Prafulla Dhariwal, Scott Gray, et al · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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There’s plenty of room at the Top: What will drive computer performance after Moore’s law?
Charles E Leiserson, Neil C Thompson, Joel S Emer, Bradley C Kuszmaul, Butler W Lampson, Daniel Sanchez, and Tao B Schardl · 2020
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Language conditioned imitation learning over unstructured data
Corey Lynch and Pierre Sermanet · 2020
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The computational limits of deep learning
Neil C Thompson, Kristjan Greenewald, Keeheon Lee, and Gabriel F Manso · 2020
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On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
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Scaling laws for deep learning
Jonathan S Rosenfeld · 2021
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On the predictability of pruning across scales
Jonathan S Rosenfeld, Jonathan Frankle, Michael Carbin, and Nir Shavit · 2021
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The decline of computers as a general purpose technology
Neil C Thompson and Svenja Spanuth · 2021
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Deep learning’s diminishing returns: The cost of improvement is becoming unsustainable
Neil C Thompson, Kristjan Greenewald, Keeheon Lee, and Gabriel F Manso · 2021
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Revisiting neural scaling laws in language and vision
Ibrahim M Alabdulmohsin, Behnam Neyshabur, and Xiaohua Zhai · 2022
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Rt-1: Robotics transformer for real-world control at scale
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Jasmine Hsu, et al · 2022
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Predictability and surprise in large generative models
Deep Ganguli, Danny Hernandez, Liane Lovitt, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova Dassarma, Dawn Drain, Nelson Elhage, et al · 2022
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Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al · 2022
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Pre-trained language models for interactive decision-making
Shuang Li, Xavier Puig, Chris Paxton, Yilun Du, Clinton Wang, Linxi Fan, Tao Chen, De-An Huang, Ekin Akyürek, Anima Anandkumar, et al · 2022
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R3m: A universal visual representation for robot manipulation
Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, and Abhinav Gupta · 2022
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Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, et al · 2022
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Estimating Training Compute of Deep Learning Models , 2022
Jaime Sevilla, Lennart Heim, Marius Hobbhan, Tamay Besiroglu, Anson Ho, and Pablo Villalobos · 2022
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Cliport: What and where pathways for robotic manipulation
Mohit Shridhar, Lucas Manuelli, and Dieter Fox · 2022
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Beyond neural scaling laws: beating power law scaling via data pruning
Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, and Ari Morcos · 2022
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The importance of (exponentially more) computing power
Neil C Thompson, Shuning Ge, and Gabriel F Manso · 2022
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Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al · 2022
Cited alongside, same era.
Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer · 2022
Cited alongside, same era.
The growing influence of industry in AI research
Nur Ahmed, Muntasir Wahed, and Neil C Thompson · 2023
Cited alongside, same era.
RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation
Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao, Coline Manon Devin, Alex X Lee, Maria Bauza Villalonga, Todor Davchev, Yuxiang Zhou, Agrim Gupta, Akhil Raju, et al · 2023
Cited alongside, same era.
Rt-2: Vision-language-action models transfer web knowledge to robotic control
Towards Synergistic, Generalized, and Efficient Dual-System for Robotic Manipulation , 2024
Qingwen Bu, Hongyang Li, Li Chen, Jisong Cai, Jia Zeng, Heming Cui, Maoqing Yao, and Yu Qiao · 2024
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Chi-Lam Cheang, Guangzeng Chen, Ya Jing, Tao Kong, Hang Li, Yifeng Li, Yuxiao Liu, Hongtao Wu, Jiafeng Xu, Yichu Yang, Hanbo Zhang, and Minzhao Zhu · 2024
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ML Papers of The Week , 2024
DAIR.AI · 2024
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AHA: A Vision-Language-Model for Detecting and Reasoning Over Failures in Robotic Manipulation
Jiafei Duan, Wilbert Pumacay, Nishanth Kumar, Yi Ru Wang, Shulin Tian, Wentao Yuan, Ranjay Krishna, Dieter Fox, Ajay Mandlekar, and Yijie Guo · 2024
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Robot utility models: General policies for zero-shot deployment in new environments
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Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, et al · 2023
Cited alongside, same era.
Open-vocabulary queryable scene representations for real world planning
Boyuan Chen, Fei Xia, Brian Ichter, Kanishka Rao, Keerthana Gopalakrishnan, Michael S Ryoo, Austin Stone, and Daniel Kappler · 2023
Cited alongside, same era.
Palm-e: An embodied multimodal language model
Danny Driess, Fei Xia, Mehdi SM Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, et al · 2023
Cited alongside, same era.
Foundation models in robotics: Applications, challenges, and the future
Roya Firoozi, Johnathan Tucker, Stephen Tian, Anirudha Majumdar, Jiankai Sun, Weiyu Liu, Yuke Zhu, Shuran Song, Ashish Kapoor, Karol Hausman, et al · 2023
Cited alongside, same era.
FLOP for Quantity, FLOP/s for Performance , 2023
Lennart Heim · 2023
Cited alongside, same era.
Scaling laws for single-agent reinforcement learning
Jacob Hilton, Jie Tang, and John Schulman · 2023
Cited alongside, same era.
Toward general-purpose robots via foundation models: A survey and meta-analysis
Yafei Hu, Quanting Xie, Vidhi Jain, Jonathan Francis, Jay Patrikar, Nikhil Keetha, Seungchan Kim, Yaqi Xie, Tianyi Zhang, Zhibo Zhao, et al · 2023
Cited alongside, same era.
Instruct2act: Mapping multi-modality instructions to robotic actions with large language model
Siyuan Huang, Zhengkai Jiang, Hao Dong, Yu Qiao, Peng Gao, and Hongsheng Li · 2023
Cited alongside, same era.
Haritheja Etukuru, Norihito Naka, Zijin Hu, Seungjae Lee, Julian Mehu, Aaron Edsinger, Chris Paxton, Soumith Chintala, Lerrel Pinto, and Nur Muhammad Mahi Shafiullah · 2024
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Awesome-Robotics-Foundation-Models , 2024
Roya Firoozi, Johnathan Tucker, Stephen Tian, Anirudha Majumdar, Jiankai Sun, Weiyu Liu, Yuke Zhu, Shuran Song, Ashish Kapoor, Karol Hausman, et al · 2024
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Mobile aloha: Learning bimanual mobile manipulation with low-cost whole-body teleoperation
Zipeng Fu, Tony Z Zhao, and Chelsea Finn · 2024
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Efficient Data Collection for Robotic Manipulation via Compositional Generalization , 2024
Jensen Gao, Annie Xie, Ted Xiao, Chelsea Finn, and Dorsa Sadigh · 2024
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robotics-fm-survey , 2024
Jeffrey Hu · 2024
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Data Engines for Humanoid Robots , 2024
Eric Jang · 2024
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r eal-world robot applications of foundation models: a review
Kento Kawaharazuka, Tatsuya Matsushima, Andrew Gambardella, Jiaxian Guo, Chris Paxton, and Andy Zeng · 2024
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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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RAM: Retrieval-Based Affordance Transfer for Generalizable Zero-Shot Robotic Manipulation , 2024
Yuxuan Kuang, Junjie Ye, Haoran Geng, Jiageng Mao, Congyue Deng, Leonidas Guibas, He Wang, and Yue Wang · 2024
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Will Scaling Solve Robotics? , 2024
Nishanth J. Kumar · 2024
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Data scaling laws in imitation learning for robotic manipulation
Fanqi Lin, Yingdong Hu, Pingyue Sheng, Chuan Wen, Jiacheng You, and Yang Gao · 2024
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Where are we in the search for an artificial visual cortex for embodied intelligence?
Arjun Majumdar, Karmesh Yadav, Sergio Arnaud, Jason Ma, Claire Chen, Sneha Silwal, Aryan Jain, Vincent-Pierre Berges, Tingfan Wu, Jay Vakil, et al · 2024
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Scaling data-constrained language models
Niklas Muennighoff, Alexander Rush, Boaz Barak, Teven Le Scao, Nouamane Tazi, Aleksandra Piktus, Sampo Pyysalo, Thomas Wolf, and Colin A Raffel · 2024
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RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots
Soroush Nasiriany, Abhiram Maddukuri, Lance Zhang, Adeet Parikh, Aaron Lo, Abhishek Joshi, Ajay Mandlekar, and Yuke Zhu · 2024
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Scaling Laws for Pre-training Agents and World Models
Tim Pearce, Tabish Rashid, Dave Bignell, Raluca Georgescu, Sam Devlin, and Katja Hofmann · 2024
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Krishan Rana, Jad Abou-Chakra, Sourav Garg, Robert Lee, Ian Reid, and Niko Suenderhauf · 2024
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Are emergent abilities of large language models a mirage?
Rylan Schaeffer, Brando Miranda, and Sanmi Koyejo · 2024
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Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters , 2024
Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar · 2024
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Embodiment-Agnostic Action Planning via Object-Part Scene Flow , 2024
Weiliang Tang, Jia-Hui Pan, Wei Zhan, Jianshu Zhou, Huaxiu Yao, Yun-Hui Liu, Masayoshi Tomizuka, Mingyu Ding, and Chi-Wing Fu · 2024
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Scaling Robot-Learning by Crowdsourcing Simulation Environments
Marcel Torne Villasevil, Arhan Jain, Vidyaaranya Macha, Jiayi Yuan, Lars Lien Ankile, Anthony Simeonov, Pulkit Agrawal, and Abhishek Gupta · 2024
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TinyVLA: Towards Fast, Data-Efficient Vision-Language-Action Models for Robotic Manipulation , 2024
Junjie Wen, Yichen Zhu, Jinming Li, Minjie Zhu, Kun Wu, Zhiyuan Xu, Ning Liu, Ran Cheng, Chaomin Shen, Yaxin Peng, Feifei Feng, and Jian Tang · 2024
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Genesis: A Generative and Universal Physics Engine for Robotics and Beyond , 2024
Zhou Xian, Yiling Qiao, Zhenjia Xu, Tsun-Hsuan Wang, Zhehuan Chen, Juntian Zheng, Ziyan Xiong, Yian Wang, Mingrui Zhang, Pingchuan Ma, Yufei Wang, Zhiyang Dou, Byungchul Kim, Yunsheng Tian, Yipu Chen, Xiaowen Qiu, Chunru Lin, Tairan He, Zilin Si, Yunchu Zhang, Zhanlue Yang, Tiantian Liu, Tianyu Li, Kashu Yamazaki, Hongxin Zhang, Huy Ha, Yu Zhang, Michael Liu, Shaokun Zheng, Zipeng Fu, Qi Wu, Yiran Geng, Feng Chen, Milky, Yuanming Hu, Chelsea Finn, Guanya Shi, Lingjie Liu, Taku Komura, Zackory Erickson, David Held, Minchen Li, Linxi ”Jim” Fan, Yuke Zhu, Wojciech Matusik, Dan Gutfreund, Shuran Song, Daniela Rus, Ming Lin, Bo Zhu, Katerina Fragkiadaki, and Chuang Gan · 2024
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Awesome-LLM-Robotics , 2024
Kira Zsolt · 2024
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AgiBot World Dataset , 2025
AgiBot · 2025
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