The Reach-Avoid Problem for Constant-Rate Multi-mode Systems. - Shankara Narayanan Krishna; Aviral Kumar; Fabio Somenzi; Behrouz Touri; Ashutosh Trivedi 001 (ATVA, 2017)

Challenges and Tool Implementation of Hybrid Rapidly-Exploring Random Trees. - Stanley Bak; Sergiy Bogomolov; Thomas A. Henzinger; Aviral Kumar (NSV@CAV, 2017)

Trainable Calibration Measures For Neural Networks From Kernel Mean Embeddings. - Aviral Kumar; Sunita Sarawagi; Ujjwal Jain (ICML, 2018)

Diagnosing Bottlenecks in Deep Q-learning Algorithms. - Justin Fu; Aviral Kumar; Matthew Soh; Sergey Levine (ICML, 2019)

Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction. - Aviral Kumar; Justin Fu; Matthew Soh; George Tucker; Sergey Levine (NeurIPS, 2019)

Graph Normalizing Flows. - Jenny Liu; Aviral Kumar; Jimmy Ba; Jamie Kiros; Kevin Swersky (NeurIPS, 2019)

Calibration of Encoder Decoder Models for Neural Machine Translation. - Aviral Kumar; Sunita Sarawagi (CoRR, 2019)

Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction. - Aviral Kumar; Justin Fu; George Tucker; Sergey Levine (CoRR, 2019)

Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning. - Xue Bin Peng; Aviral Kumar; Grace Zhang; Sergey Levine (CoRR, 2019)

Reward-Conditioned Policies. - Aviral Kumar; Xue Bin Peng; Sergey Levine (CoRR, 2019)

Chaining Behaviors from Data with Model-Free Reinforcement Learning. - Avi Singh; Albert Yu 002; Jonathan Yang; Jesse Zhang; Aviral Kumar; Sergey Levine (CoRL, 2020)

DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction. - Aviral Kumar; Abhishek Gupta 004; Sergey Levine (NeurIPS, 2020)

One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL. - Saurabh Kumar 004; Aviral Kumar; Sergey Levine; Chelsea Finn (NeurIPS, 2020)

Model Inversion Networks for Model-Based Optimization. - Aviral Kumar; Sergey Levine (NeurIPS, 2020)

Conservative Q-Learning for Offline Reinforcement Learning. - Aviral Kumar; Aurick Zhou; George Tucker; Sergey Levine (NeurIPS, 2020)

D4RL: Datasets for Deep Data-Driven Reinforcement Learning. - Justin Fu; Aviral Kumar; Ofir Nachum; George Tucker; Sergey Levine (CoRR, 2020)

Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems. - Sergey Levine; Aviral Kumar; George Tucker; Justin Fu (CoRR, 2020)

COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning. - Avi Singh; Albert Yu 002; Jonathan Yang; Jesse Zhang; Aviral Kumar; Sergey Levine (CoRR, 2020)

A Workflow for Offline Model-Free Robotic Reinforcement Learning. - Aviral Kumar; Anikait Singh; Stephen Tian; Chelsea Finn; Sergey Levine (CoRL, 2021)

OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning. - Anurag Ajay; Aviral Kumar; Pulkit Agrawal 001; Sergey Levine; Ofir Nachum (ICLR, 2021)

Conservative Safety Critics for Exploration. - Homanga Bharadhwaj; Aviral Kumar; Nicholas Rhinehart; Sergey Levine; Florian Shkurti; Animesh Garg (ICLR, 2021)

Benchmarks for Deep Off-Policy Evaluation. - Justin Fu; Mohammad Norouzi 002; Ofir Nachum; George Tucker; Ziyu Wang 001; Alexander Novikov 001; Mengjiao Yang 001; Michael R. Zhang; Yutian Chen 001; Aviral Kumar; Cosmin Paduraru; Sergey Levine; et al. (ICLR, 2021)

Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning. - Aviral Kumar; Rishabh Agarwal; Dibya Ghosh; Sergey Levine (ICLR, 2021)

Conservative Objective Models for Effective Offline Model-Based Optimization. - Brandon Trabucco; Aviral Kumar; Xinyang Geng; Sergey Levine (ICML, 2021)

Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability. - Dibya Ghosh; Jad Rahme; Aviral Kumar; Amy Zhang 001; Ryan P. Adams; Sergey Levine (NeurIPS, 2021)

Conservative Data Sharing for Multi-Task Offline Reinforcement Learning. - Tianhe Yu; Aviral Kumar; Yevgen Chebotar; Karol Hausman; Sergey Levine; Chelsea Finn (NeurIPS, 2021)

COMBO: Conservative Offline Model-Based Policy Optimization. - Tianhe Yu; Aviral Kumar; Rafael Rafailov; Aravind Rajeswaran; Sergey Levine; Chelsea Finn (NeurIPS, 2021)

Don't Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning. - Homer Walke; Jonathan Yang; Albert Yu 002; Aviral Kumar; Jedrzej Orbik; Avi Singh; Sergey Levine (CoRL, 2022)

DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization. - Aviral Kumar; Rishabh Agarwal; Tengyu Ma 001; Aaron C. Courville; George Tucker; Sergey Levine (ICLR, 2022)

Should I Run Offline Reinforcement Learning or Behavioral Cloning? - Aviral Kumar; Joey Hong; Anikait Singh; Sergey Levine (ICLR, 2022)

Data-Driven Offline Optimization for Architecting Hardware Accelerators. - Aviral Kumar; Amir Yazdanbakhsh; Milad Hashemi; Kevin Swersky; Sergey Levine (ICLR, 2022)

Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization. - Brandon Trabucco; Xinyang Geng; Aviral Kumar; Sergey Levine (ICML, 2022)

How to Leverage Unlabeled Data in Offline Reinforcement Learning. - Tianhe Yu; Aviral Kumar; Yevgen Chebotar; Karol Hausman; Chelsea Finn; Sergey Levine (ICML, 2022)

Data-Driven Offline Decision-Making via Invariant Representation Learning. - Han Qi; Yi Su; Aviral Kumar; Sergey Levine (NeurIPS, 2022)

DASCO: Dual-Generator Adversarial Support Constrained Offline Reinforcement Learning. - Quan Vuong; Aviral Kumar; Sergey Levine; Yevgen Chebotar (NeurIPS, 2022)

Off-Policy Actor-critic for Recommender Systems. - Minmin Chen; Can Xu; Vince Gatto; Devanshu Jain; Aviral Kumar; Ed H. Chi (RecSys, 2022)

When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning? - Aviral Kumar; Joey Hong; Anikait Singh; Sergey Levine (CoRR, 2022)

Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials. - Aviral Kumar; Anikait Singh; Frederik Ebert; Yanlai Yang; Chelsea Finn; Sergey Levine (CoRR, 2022)

Offline RL With Realistic Datasets: Heteroskedasticity and Support Constraints. - Anikait Singh; Aviral Kumar; Quan Vuong; Yevgen Chebotar; Sergey Levine (CoRR, 2022)

Dual Generator Offline Reinforcement Learning. - Quan Vuong; Aviral Kumar; Sergey Levine; Yevgen Chebotar (CoRR, 2022)

Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions. - Yevgen Chebotar; Quan Vuong; Karol Hausman; Fei Xia 002; Yao Lu 006; Alex Irpan; Aviral Kumar; Tianhe Yu; Alexander Herzog; Karl Pertsch; Keerthana Gopalakrishnan; Julian Ibarz; et al. (CoRL, 2023)

Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning. - Jianlan Luo; Perry Dong; Jeffrey Wu; Aviral Kumar; Xinyang Geng; Sergey Levine (CoRL, 2023)

Confidence-Conditioned Value Functions for Offline Reinforcement Learning. - Joey Hong; Aviral Kumar; Sergey Levine (ICLR, 2023)

Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes. - Aviral Kumar; Rishabh Agarwal; Xinyang Geng; George Tucker; Sergey Levine (ICLR, 2023)

Efficient Deep Reinforcement Learning Requires Regulating Overfitting. - Qiyang Li; Aviral Kumar; Ilya Kostrikov; Sergey Levine (ICLR, 2023)

Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets. - Zhang-Wei Hong; Aviral Kumar; Sathwik Karnik; Abhishek Bhandwaldar; Akash Srivastava; Joni Pajarinen; Romain Laroche; Abhishek Gupta 004; Pulkit Agrawal 001 (NeurIPS, 2023)

Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning. - Mitsuhiko Nakamoto; Simon Zhai; Anikait Singh; Max Sobol Mark; Yi Ma 001; Chelsea Finn; Aviral Kumar; Sergey Levine (NeurIPS, 2023)

ReDS: Offline RL With Heteroskedastic Datasets via Support Constraints. - Anikait Singh; Aviral Kumar; Quan Vuong; Yevgen Chebotar; Sergey Levine (NeurIPS, 2023)

Pre-Training for Robots: Offline RL Enables Learning New Tasks in a Handful of Trials. - Aviral Kumar; Anikait Singh; Frederik D. Ebert; Mitsuhiko Nakamoto; Yanlai Yang; Chelsea Finn; Sergey Levine (Robotics: Science and Systems, 2023)

Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning. - Mitsuhiko Nakamoto; Yuexiang Zhai; Anikait Singh; Max Sobol Mark; Yi Ma 001; Chelsea Finn; Aviral Kumar; Sergey Levine (CoRR, 2023)

Robotic Offline RL from Internet Videos via Value-Function Pre-Training. - Chethan Bhateja; Derek Guo; Dibya Ghosh; Anikait Singh; Manan Tomar; Quan Vuong; Yevgen Chebotar; Sergey Levine; Aviral Kumar (CoRR, 2023)

Latent Conservative Objective Models for Data-Driven Crystal Structure Prediction. - Han Qi; Xinyang Geng; Stefano Rando; Iku Ohama; Aviral Kumar; Sergey Levine (CoRR, 2023)

Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models. - Kevin Black; Mitsuhiko Nakamoto; Pranav Atreya; Homer Walke; Chelsea Finn; Aviral Kumar; Sergey Levine (CoRR, 2023)

Steering Your Generalists: Improving Robotic Foundation Models via Value Guidance. - Mitsuhiko Nakamoto; Oier Mees; Aviral Kumar; Sergey Levine (CoRL, 2024)

Zero-Shot Robotic Manipulation with Pre-Trained Image-Editing Diffusion Models. - Kevin Black; Mitsuhiko Nakamoto; Pranav Atreya; Homer Rich Walke; Chelsea Finn; Aviral Kumar; Sergey Levine (ICLR, 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 (ICML, 2024)

Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data. - Fahim Tajwar; Anikait Singh; Archit Sharma; Rafael Rafailov; Jeff Schneider 001; Tengyang Xie; Stefano Ermon; Chelsea Finn; Aviral Kumar (ICML, 2024)

ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL. - Yifei Zhou; Andrea Zanette; Jiayi Pan; Sergey Levine; Aviral Kumar (ICML, 2024)

Robotic Offline RL from Internet Videos via Value-Function Learning. - Chethan Bhateja; Derek Guo; Dibya Ghosh; Anikait Singh; Manan Tomar; Quan Vuong; Yevgen Chebotar; Sergey Levine; Aviral Kumar (ICRA, 2024)

DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning. - Hao Bai; Yifei Zhou; Jiayi Pan; Mert Cemri; Alane Suhr; Sergey Levine; Aviral Kumar (NeurIPS, 2024)

Is Value Learning Really the Main Bottleneck in Offline RL? - Seohong Park; Kevin Frans; Sergey Levine; Aviral Kumar (NeurIPS, 2024)

Recursive Introspection: Teaching Language Model Agents How to Self-Improve. - Yuxiao Qu; Tianjun Zhang; Naman Garg; Aviral Kumar (NeurIPS, 2024)

Designing Cell-Type-Specific Promoter Sequences Using Conservative Model-Based Optimization. - Aniketh Janardhan Reddy; Xinyang Geng; Michael Herschl; Sathvik Kolli; Aviral Kumar; Patrick Hsu; Sergey Levine; Nilah Ioannidis (NeurIPS, 2024)

RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. - Amrith Setlur; Saurabh Garg; Xinyang Geng; Naman Garg; Virginia Smith; Aviral Kumar (NeurIPS, 2024)

D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning. - Rafael Rafailov; Kyle Beltran Hatch; Anikait Singh; Aviral Kumar; Laura M. Smith; Ilya Kostrikov; Philippe Hansen-Estruch; Victor Kolev; Philip J. Ball; Jiajun Wu 001; Sergey Levine; Chelsea Finn (RLJ, 2024)

Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters. - Charlie Snell; Jaehoon Lee 001; Kelvin Xu; Aviral Kumar (CoRR, 2024)

RRM: Robust Reward Model Training Mitigates Reward Hacking. - Tianqi Liu 002; Wei Xiong 015; Jie Ren 006; Lichang Chen; Junru Wu; Rishabh Joshi; Yang Gao; Jiaming Shen; Zhen Qin 001; Tianhe Yu; Daniel Sohn; Anastasiia Makarova; et al. (CoRR, 2024)

What Do Learning Dynamics Reveal About Generalization in LLM Reasoning? - Katie Kang; Amrith Setlur; Dibya Ghosh; Jacob Steinhardt; Claire J. Tomlin; Sergey Levine; Aviral Kumar (CoRR, 2024)

Policy Agnostic RL: Offline RL and Online RL Fine-Tuning of Any Class and Backbone. - Max Sobol Mark; Tian Gao; Georgia Gabriela Sampaio; Mohan Kumar Srirama; Archit Sharma; Chelsea Finn; Aviral Kumar (CoRR, 2024)

RRM: Robust Reward Model Training Mitigates Reward Hacking. - Tianqi Liu 002; Wei Xiong 015; Jie Ren 006; Lichang Chen; Junru Wu; Rishabh Joshi; Yang Gao; Jiaming Shen; Zhen Qin 001; Tianhe Yu; Daniel Sohn; Anastasia Makarova; et al. (ICLR, 2025)

Digi-Q: Learning VLM Q-Value Functions for Training Device-Control Agents. - Hao Bai; Yifei Zhou; Li Erran Li; Sergey Levine; Aviral Kumar (ICLR, 2025)

Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models. - Yinlam Chow; Guy Tennenholtz; Izzeddin Gur; Vincent Zhuang; Bo Dai 001; Aviral Kumar; Rishabh Agarwal; Sridhar Thiagarajan; Craig Boutilier; Aleksandra Faust (ICLR, 2025)

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; et al. (ICLR, 2025)

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 (ICLR, 2025)

Scaling LLM Test-Time Compute Optimally Can be More Effective than Scaling Parameters for Reasoning. - Charlie Victor Snell; Jaehoon Lee 001; Kelvin Xu; Aviral Kumar (ICLR, 2025)

Generative Verifiers: Reward Modeling as Next-Token Prediction. - Lunjun Zhang; Arian Hosseini; Hritik Bansal; Mehran Kazemi; Aviral Kumar; Rishabh Agarwal (ICLR, 2025)

Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline Data. - Zhiyuan Zhou; Andy Peng; Qiyang Li; Sergey Levine; Aviral Kumar (ICLR, 2025)

Unfamiliar Finetuning Examples Control How Language Models Hallucinate. - Katie Kang; Eric Wallace; Claire J. Tomlin; Aviral Kumar; Sergey Levine (NAACL (Long Papers), 2025)

Vision-Language Models Provide Promptable Representations for Reinforcement Learning. - William Chen; Oier Mees; Aviral Kumar; Sergey Levine (Trans. Mach. Learn. Res., 2025)

Value-Based Deep RL Scales Predictably. - Oleh Rybkin; Michal Nauman; Preston Fu; Charlie Snell; Pieter Abbeel; Sergey Levine; Aviral Kumar (CoRR, 2025)

Scaling Test-Time Compute Without Verification or RL is Suboptimal. - Amrith Setlur; Nived Rajaraman; Sergey Levine; Aviral Kumar (CoRR, 2025)

Digi-Q: Learning Q-Value Functions for Training Device-Control Agents. - Hao Bai; Yifei Zhou; Li Erran Li; Sergey Levine; Aviral Kumar (CoRR, 2025)

Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning. - Yuxiao Qu; Matthew Y. R. Yang; Amrith Setlur; Lewis Tunstall; Edward Emanuel Beeching; Ruslan Salakhutdinov; Aviral Kumar (CoRR, 2025)

Bigger, Regularized, Categorical: High-Capacity Value Functions are Efficient Multi-Task Learners. - Michal Nauman; Marek Cygan; Carmelo Sferrazza; Aviral Kumar; Pieter Abbeel (CoRR, 2025)

Grounded Reinforcement Learning for Visual Reasoning. - Gabriel Sarch; Snigdha Saha; Naitik Khandelwal; Ayush Jain; Michael J. Tarr; Aviral Kumar; Katerina Fragkiadaki (CoRR, 2025)

Horizon Reduction Makes RL Scalable. - Seohong Park; Kevin Frans; Deepinder Mann; Benjamin Eysenbach; Aviral Kumar; Sergey Levine (CoRR, 2025)

Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction. - Junhong Shen; Hao Bai; Lunjun Zhang; Yifei Zhou; Amrith Setlur; Shengbang Tong; Diego Caples; Nan Jiang 008; Tong Zhang 001; Ameet Talwalkar; Aviral Kumar (CoRR, 2025)

e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs. - Amrith Setlur; Matthew Y. R. Yang; Charlie Snell; Jeremy Greer; Ian Wu; Virginia Smith; Max Simchowitz; Aviral Kumar (CoRR, 2025)

Reasoning as an Adaptive Defense for Safety. - Taeyoun Kim; Fahim Tajwar; Aditi Raghunathan; Aviral Kumar (CoRR, 2025)

Compute-Optimal Scaling for Value-Based Deep RL. - Preston Fu; Oleh Rybkin; Zhiyuan Zhou; Michal Nauman; Pieter Abbeel; Sergey Levine; Aviral Kumar (CoRR, 2025)