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

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

RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control. - Brianna Zitkovich; Tianhe Yu; Sichun Xu; Peng Xu 010; Ted Xiao; Fei Xia 002; Jialin Wu; Paul Wohlhart; Stefan Welker; Ayzaan Wahid; Quan Vuong; Vincent Vanhoucke; et al. (CoRL, 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)

Open X-Embodiment: Robotic Learning Datasets and RT-X Models. - Open X.-Embodiment Collaboration; Abhishek Padalkar; Acorn Pooley; Ajinkya Jain; Alex Bewley; Alexander Herzog; Alex Irpan; Alexander Khazatsky; Anant Raj; Anikait Singh; Anthony Brohan; Antonin Raffin; et al. (CoRR, 2023)

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)

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)

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration. - Abby O'Neill; Abdul Rehman; Abhiram Maddukuri; Abhishek Gupta 004; Abhishek Padalkar; Abraham Lee; Acorn Pooley; Agrim Gupta; Ajay Mandlekar; Ajinkya Jain; Albert Tung; Alex Bewley; et al. (ICRA, 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)

Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation. - Rohin Manvi; Anikait Singh; Stefano Ermon (CoRR, 2024)

Test-Time Alignment via Hypothesis Reweighting. - Yoonho Lee 001; Jonathan Williams; Henrik Marklund; Archit Sharma; Eric Mitchell; Anikait Singh; Chelsea Finn (CoRR, 2024)

Personalized Preference Fine-tuning of Diffusion Models. - Meihua Dang; Anikait Singh; Linqi Zhou; Stefano Ermon; Jiaming Song (CVPR, 2025)

Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought. - Violet Xiang; Charlie Snell; Kanishk Gandhi; Alon Albalak; Anikait Singh; Chase Blagden; Duy Phung; Rafael Rafailov; Nathan Lile; Dakota Mahan; Louis Castricato; Jan-Philipp Fränken; et al. (CoRR, 2025)

Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models. - Alon Albalak; Duy Phung; Nathan Lile; Rafael Rafailov; Kanishk Gandhi; Louis Castricato; Anikait Singh; Chase Blagden; Violet Xiang; Dakota Mahan; Nick Haber (CoRR, 2025)

FSPO: Few-Shot Preference Optimization of Synthetic Preference Data in LLMs Elicits Effective Personalization to Real Users. - Anikait Singh; Sheryl Hsu; Kyle Hsu; Eric Mitchell; Stefano Ermon; Tatsunori Hashimoto; Archit Sharma; Chelsea Finn (CoRR, 2025)

Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs. - Kanishk Gandhi; Ayush Chakravarthy; Anikait Singh; Nathan Lile; Noah D. Goodman (CoRR, 2025)