Rishabh Agarwal

According to our database, Rishabh Agarwal authored at least 56 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models.

Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron T. Parisi, Abhishek Kumar, Alexander A. Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin Fathy Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel

Trans. Mach. Learn. Res., 2024

A comprehensive survey on answer generation methods using NLP.

Prashant Upadhyay, Rishabh Agarwal, Sumeet Dhiman, Abhinav Sarkar, Saumya Chaturvedi

Nat. Lang. Process. J., 2024

Evolving Alignment via Asymmetric Self-Play.

Ziyu Ye, Rishabh Agarwal, Tianqi Liu, Rishabh Joshi, Sarmishta Velury, Quoc V. Le, Qijun Tan, Yuan Liu

CoRR, 2024

Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language Models.

Michael Noukhovitch, Shengyi Huang, Sophie Xhonneux, Arian Hosseini, Rishabh Agarwal, Aaron C. Courville

CoRR, 2024

Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Sampling.

Wenda Xu, Rujun Han, Zifeng Wang, Long T. Le, Dhruv Madeka, Lei Li, William Yang Wang, Rishabh Agarwal, Chen-Yu Lee, Tomas Pfister

CoRR, 2024

Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning.

Amrith Setlur, Chirag Nagpal, Adam Fisch, Xinyang Geng, Jacob Eisenstein, Rishabh Agarwal, Alekh Agarwal, Jonathan Berant, Aviral Kumar

CoRR, 2024

Not All LLM Reasoners Are Created Equal.

Arian Hosseini, Alessandro Sordoni, Daniel Toyama, Aaron C. Courville, Rishabh Agarwal

CoRR, 2024

Training Language Models to Self-Correct via Reinforcement Learning.

Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, Yi Su, John D. Co-Reyes, Avi Singh, Kate Baumli, Shariq Iqbal, Colton Bishop, Rebecca Roelofs, Lei M. Zhang, Kay McKinney, Disha Shrivastava, Cosmin Paduraru, George Tucker, Doina Precup, Feryal M. P. Behbahani, Aleksandra Faust

CoRR, 2024

Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling.

Hritik Bansal, Arian Hosseini, Rishabh Agarwal, Vinh Q. Tran, Mehran Kazemi

CoRR, 2024

Generative Verifiers: Reward Modeling as Next-Token Prediction.

Lunjun Zhang, Arian Hosseini, Hritik Bansal, Mehran Kazemi, Aviral Kumar, Rishabh Agarwal

CoRR, 2024

Don't Throw Away Data: Better Sequence Knowledge Distillation.

Jun Wang, Eleftheria Briakou, Hamid Dadkhahi, Rishabh Agarwal, Colin Cherry, Trevor Cohn

CoRR, 2024

On scalable oversight with weak LLMs judging strong LLMs.

Zachary Kenton, Noah Y. Siegel, János Kramár, Jonah Brown-Cohen, Samuel Albanie, Jannis Bulian, Rishabh Agarwal, David Lindner, Yunhao Tang, Noah D. Goodman, Rohin Shah

CoRR, 2024

Many-Shot In-Context Learning.

Rishabh Agarwal, Avi Singh, Lei M. Zhang, Bernd Bohnet, Stephanie Chan, Ankesh Anand, Zaheer Abbas, Azade Nova, John D. Co-Reyes, Eric Chu, Feryal M. P. Behbahani, Aleksandra Faust, Hugo Larochelle

CoRR, 2024

Transformers Can Achieve Length Generalization But Not Robustly.

Yongchao Zhou, Uri Alon, Xinyun Chen, Xuezhi Wang, Rishabh Agarwal, Denny Zhou

CoRR, 2024

V-STaR: Training Verifiers for Self-Taught Reasoners.

Arian Hosseini, Xingdi Yuan, Nikolay Malkin, Aaron C. Courville, Alessandro Sordoni, Rishabh Agarwal

CoRR, 2024

SiT: Symmetry-invariant Transformers for Generalisation in Reinforcement Learning.

Matthias Weissenbacher, Rishabh Agarwal, Yoshinobu Kawahara

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Stop Regressing: Training Value Functions via Classification for Scalable Deep RL.

Jesse Farebrother, Jordi Orbay, Quan Vuong, Adrien Ali Taïga, Yevgen Chebotar, Ted Xiao, Alex Irpan, Sergey Levine, Pablo Samuel Castro, Aleksandra Faust, Aviral Kumar, Rishabh Agarwal

Proceedings of the Forty-first International Conference on Machine Learning, 2024

DistillSpec: Improving Speculative Decoding via Knowledge Distillation.

Yongchao Zhou, Kaifeng Lyu, Ankit Singh Rawat, Aditya Krishna Menon, Afshin Rostamizadeh, Sanjiv Kumar, Jean-François Kagy, Rishabh Agarwal

Proceedings of the Twelfth International Conference on Learning Representations, 2024

On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes.

Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, Piotr Stanczyk, Sabela Ramos Garea, Matthieu Geist, Olivier Bachem

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy.

Max Schwarzer, Jesse Farebrother, Joshua Greaves, Ekin Dogus Cubuk, Rishabh Agarwal, Aaron C. Courville, Marc G. Bellemare, Sergei V. Kalinin, Igor Mordatch, Pablo Samuel Castro, Kevin M. Roccapriore

CoRR, 2023

GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models.

Rishabh Agarwal, Nino Vieillard, Piotr Stanczyk, Sabela Ramos, Matthieu Geist, Olivier Bachem

CoRR, 2023

Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research.

Cole Gulino, Justin Fu, Wenjie Luo, George Tucker, Eli Bronstein, Yiren Lu, Jean Harb, Xinlei Pan, Yan Wang, Xiangyu Chen, John D. Co-Reyes, Rishabh Agarwal, Rebecca Roelofs, Yao Lu, Nico Montali, Paul Mougin, Zoey Yang, Brandyn White, Aleksandra Faust, Rowan McAllister, Dragomir Anguelov, Benjamin Sapp

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Revisiting Bellman Errors for Offline Model Selection.

Joshua P. Zitovsky, Daniel de Marchi, Rishabh Agarwal, Michael Rene Kosorok

Proceedings of the International Conference on Machine Learning, 2023

The Dormant Neuron Phenomenon in Deep Reinforcement Learning.

Ghada Sokar, Rishabh Agarwal, Pablo Samuel Castro, Utku Evci

Proceedings of the International Conference on Machine Learning, 2023

Bigger, Better, Faster: Human-level Atari with human-level efficiency.

Max Schwarzer, Johan Samir Obando-Ceron, Aaron C. Courville, Marc G. Bellemare, Rishabh Agarwal, Pablo Samuel Castro

Proceedings of the International Conference on Machine Learning, 2023

Bootstrapped Representations in Reinforcement Learning.

Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney

Proceedings of the International Conference on Machine Learning, 2023

Investigating Multi-task Pretraining and Generalization in Reinforcement Learning.

Adrien Ali Taïga, Rishabh Agarwal, Jesse Farebrother, Aaron C. Courville, Marc G. Bellemare

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Revisiting Bisimulation: A Sampling-Based State Similarity Pseudo-metric.

Charline Le Lan, Rishabh Agarwal

Proceedings of the First Tiny Papers Track at ICLR 2023, 2023

Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes.

Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, Sergey Levine

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks.

Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, Marc G. Bellemare

Proceedings of the Eleventh International Conference on Learning Representations, 2023

A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces.

Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa, Rishabh Agarwal, Marc G. Bellemare

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Beyond Tabula Rasa: Reincarnating Reinforcement Learning.

Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare

CoRR, 2022

Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress.

Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Detection Of Crop Water Stress In Maize Using Drone Based Hyperspectral Imaging.

Jayantrao Mohite, Suryakant A. Sawant, Rishabh Agarwal, Ankur Pandit, Srinivasu Pappula

Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2022

DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization.

Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron C. Courville, George Tucker, Sergey Levine

Proceedings of the Tenth International Conference on Learning Representations, 2022

On the Generalization of Representations in Reinforcement Learning.

Charline Le Lan, Stephen Tu, Adam Oberman, Rishabh Agarwal, Marc G. Bellemare

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation.

Evgenii Nikishin, Romina Abachi, Rishabh Agarwal, Pierre-Luc Bacon

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Deep Reinforcement Learning at the Edge of the Statistical Precipice.

Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, Marc G. Bellemare

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Neural Additive Models: Interpretable Machine Learning with Neural Nets.

Rishabh Agarwal, Levi Melnick, Nicholas Frosst, Xuezhou Zhang, Benjamin J. Lengerich, Rich Caruana, Geoffrey E. Hinton

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning.

Aviral Kumar, Rishabh Agarwal, Dibya Ghosh, Sergey Levine

Proceedings of the 9th International Conference on Learning Representations, 2021

Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning.

Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G. Bellemare

Proceedings of the 9th International Conference on Learning Representations, 2021

Field Boundary Identification using Convolutional Neural Network and GIS on High Resolution Satellite Observations.

Suryakant A. Sawant, Rishabh Agarwal, Jayantrao Mohite, Ankur Pandit, Srinivasu Pappula

Proceedings of the 9th International Conference on Agro-Geoinformatics, 2021

2020

RL Unplugged: Benchmarks for Offline Reinforcement Learning.

Çaglar Gülçehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matt Hoffman, Ofir Nachum, George Tucker, Nicolas Heess, Nando de Freitas

CoRR, 2020

Neural Additive Models: Interpretable Machine Learning with Neural Nets.

Rishabh Agarwal, Nicholas Frosst, Xuezhou Zhang, Rich Caruana, Geoffrey E. Hinton

CoRR, 2020

IITK at SemEval-2020 Task 10: Transformers for Emphasis Selection.

Vipul Singhal, Sahil Dhull, Rishabh Agarwal, Ashutosh Modi

Proceedings of the Fourteenth Workshop on Semantic Evaluation, 2020

RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning.

Çaglar Gülçehre, Ziyu Wang, Alexander Novikov, Thomas Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matthew Hoffman, Nicolas Heess, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Revisiting Fundamentals of Experience Replay.

William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney

Proceedings of the 37th International Conference on Machine Learning, 2020

An Optimistic Perspective on Offline Reinforcement Learning.

Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Striving for Simplicity in Off-policy Deep Reinforcement Learning.

Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi

CoRR, 2019

Evaluation Function Approximation for Scrabble.

Rishabh Agarwal

CoRR, 2019

Measurement of shear forces during gripping tasks with a low-cost tactile sensing system.

Rishabh Agarwal, Sarah Bergbreiter

Proceedings of the IEEE International Conference on Soft Robotics, 2019

Learning to Generalize from Sparse and Underspecified Rewards.

Rishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzi

Proceedings of the 36th International Conference on Machine Learning, 2019

2017

Computing Theory Prime Implicates in Modal Logic.

Manoj K. Raut, Tushar V. Kokane, Rishabh Agarwal

Proceedings of the Intelligent Systems Design and Applications, 2017

S-Pencil: A Smart Pencil Grip Monitoring System for Kids Using Sensors.

Prakhar Gupta, Rishabh Agarwal, Surbhi Saraswat, Hari Prabhat Gupta, Tanima Dutta

Proceedings of the 2017 IEEE Global Communications Conference, 2017

Development of a Low-Cost Education Platform: RoboMuse 4.0.

Ayush Shukla, Rishabjit Singh, Rishabh Agarwal, Muhammad Suhail, Subir K. Saha, Santanu Chaudhury

Proceedings of the Advances in Robotics, 2017

2016

Touchless human-mobile robot interaction using a projectable interactive surface.

Rishabh Agarwal, Prayag Sharma, Subir K. Saha, Takafumi Matsumaru

Proceedings of the 2016 IEEE/SICE International Symposium on System Integration, 2016