Divide-and-Conquer Reinforcement Learning. - Dibya Ghosh; Avi Singh; Aravind Rajeswaran; Vikash Kumar; Sergey Levine (ICLR (Poster), 2018)

Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition. - Justin Fu; Avi Singh; Dibya Ghosh; Larry Yang; Sergey Levine (NeurIPS, 2018)

Learning Actionable Representations with Goal Conditioned Policies. - Dibya Ghosh; Abhishek Gupta 004; Sergey Levine (ICLR (Poster), 2019)

Learning To Reach Goals Without Reinforcement Learning. - Dibya Ghosh; Abhishek Gupta 004; Justin Fu; Ashwin Reddy; Coline Devin; Benjamin Eysenbach; Sergey Levine (CoRR, 2019)

Representations for Stable Off-Policy Reinforcement Learning. - Dibya Ghosh; Marc G. Bellemare (ICML, 2020)

An operator view of policy gradient methods. - Dibya Ghosh; Marlos C. Machado; Nicolas Le Roux (NeurIPS, 2020)

On Catastrophic Interference in Atari 2600 Games. - William Fedus; Dibya Ghosh; John D. Martin; Marc G. Bellemare; Yoshua Bengio; Hugo Larochelle (CoRR, 2020)

Learning to Reach Goals via Iterated Supervised Learning. - Dibya Ghosh; Abhishek Gupta 004; Ashwin Reddy; Justin Fu; Coline Manon Devin; Benjamin Eysenbach; Sergey Levine (ICLR, 2021)

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

Offline RL Policies Should Be Trained to be Adaptive. - Dibya Ghosh; Anurag Ajay; Pulkit Agrawal 001; Sergey Levine (ICML, 2022)

Distributionally Adaptive Meta Reinforcement Learning. - Anurag Ajay; Abhishek Gupta 004; Dibya Ghosh; Sergey Levine; Pulkit Agrawal 001 (NeurIPS, 2022)

Reinforcement Learning from Passive Data via Latent Intentions. - Dibya Ghosh; Chethan Anand Bhateja; Sergey Levine (ICML, 2023)

Accelerating Exploration with Unlabeled Prior Data. - Qiyang Li; Jason Zhang; Dibya Ghosh; Amy Zhang 001; Sergey Levine (NeurIPS, 2023)

HIQL: Offline Goal-Conditioned RL with Latent States as Actions. - Seohong Park; Dibya Ghosh; Benjamin Eysenbach; Sergey Levine (NeurIPS, 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)

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)

Octo: An Open-Source Generalist Robot Policy. - Dibya Ghosh; Homer Rich Walke; Karl Pertsch; Kevin Black; Oier Mees; Sudeep Dasari; Joey Hejna; Tobias Kreiman; Charles Xu 003; Jianlan Luo; You Liang Tan; Lawrence Yunliang Chen; et al. (Robotics: Science and Systems, 2024)

Octo: An Open-Source Generalist Robot Policy. - Octo Model Team; Dibya Ghosh; Homer Walke; Karl Pertsch; Kevin Black; Oier Mees; Sudeep Dasari; Joey Hejna; Tobias Kreiman; Charles Xu 003; Jianlan Luo; You Liang Tan; 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)

ViVa: Video-Trained Value Functions for Guiding Online RL from Diverse Data. - Nitish Dashora; Dibya Ghosh; Sergey Levine (CoRR, 2025)

π0.5: 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; Manuel Y. Galliker; Dibya Ghosh; et al. (CoRR, 2025)

Visual Pre-Training on Unlabeled Images using Reinforcement Learning. - Dibya Ghosh; Sergey Levine (CoRR, 2025)