news | Benjamin Eysenbach

Jun 7, 2026	Princeton RL @ ICLR 2026! Demystifying the Mechanisms Behind Emergent Exploration in Goal-conditioned RL, led by Mahsa Bastankhah and Grace Liu. Interactive demo! Value Flows, led by Perry Dong and Chongyi Zheng. Intention-Conditioned Flow Occupancy Models, led by Chongyi Zheng. SLAP: Shortcut Learning for Abstract Planning, led by Isabel Liu. Temporal Representations for Exploration: Learning Complex Exploratory Behavior without Extrinsic Rewards, led by Faisal Mohamed. Scaling Goal-conditioned Reinforcement Learning with Multistep Quasimetric Distances, led by Bill Zheng. Occupancy Reward Shaping: Improving Credit Assignment for Offline Goal-Conditioned Reinforcement Learning, led by Aravind Venugopal. Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning, led by Patrik Reizinger, Bálint Mucsányi and Siyuan Guo.
Mar 15, 2026	Looking forward to sharing research from our group at upcoming workshops! Frontiers in Online Reinforcement Learning @ IMSI. (Recording coming soon) Physical Foundations of Intelligent Systems @ Princeton AI Lab.
Feb 15, 2026	Honored to receive the Sloan Fellowship!
Jan 2, 2026	Honored and excited to receive a grant from Google to study Accelerating Reinforcement Learning in Hard-To-Verify Domains via Empowerment and Skills.
Dec 1, 2025	Princeton RL @ NeurIPS 2025! I’m excited to share progress we’ve made in RL algorithms (and the many problems still unsolved): 1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities, led by Kevin Wang and Ishaan Javali and Michał Bortkiewicz. (oral) Normalizing Flows are Capable Models for RL. Led by Raj Ghugare. Contrastive Representations for Temporal Reasoning Led by Alicja Ziarko, with Michal Bortkiewicz, Michal Zawalski, and Piotr Milos. Horizon Reduction Makes Offline RL Scalable. Let by Seohong Park, with Kevin Frans, Deepinder Mann, Aviral Kumar, Sergey Levine. (spotlight) We’re also presenting preliminary work at several of the workshops: Low-Rank Successor Representations Capture Human-Like Generalization. Led by Eva Yi Xie, with Nathaniel D. Daw. (Unifying Representations in Neural Models) Structured Response Diversity with Mutual Information. Led by Devan Shah, Owen Yang, and Daniel Yang, with Chongyi Zheng. (Scaling Environments for Agents) Demystifying emergent exploration in goal-conditioned RL. Led by Mahsa Bastankhah and Grace Liu, with Dilip Arumugam and Thomas L. Griffiths. (Aligning Reinforcement Learning Experimentalists and Theorists; Interpreting Cognition in Deep Learning Models) Training LLM Agents to Empower Humans. Evan Ellis, Vivek Myers, Jens Tuyls, Sergey Levine, Anca Dragan. (Deep Learning for Code) Unsupervised Contrastive Goal Reaching. Led by Ahmed Turkman, with Raj Ghugare. (Aligning Reinforcement Learning Experimentalists and Theorists) Combinatorial Representations for Temporal Reasoning. Led by Alicja Ziarko, with Michał Bortkiewicz, Michał Zawalski, Piotr Miłoś. (Differentiable Learning of Combinatorial Algorithms; Unifying Representations in Neural Models; WiML) Horizon Reduction Makes Offline RL Scalable. Led by Seohong Park, with Kevin Frans, Deepinder Mann, Aviral Kumar, Sergey Levine. (Aligning Reinforcement Learning Experimentalists and Theorists) Finally, I’m organizing a NeurIPS 2025 workshop, Data on the Brain & Mind, together with Eva Yi Xie, Catherine Ji, Vivek Myers, Archer Wang, Mahsa Bastankhah, Jenelle Feather, Erin Grant, and Richard Gao. On December 7th.
Oct 2, 2025	Honored and excited to receive a GPU grant from NVIDIA for research into Interactive Generative Models that Learn via Discovery, not Data.
Jul 15, 2025	Awarded the NSF CAREER award for Unsupervised and Autonomous Reinforcement Learning of Skills
Jul 14, 2025	I gave an ICML tutorial on generative AI and reinforcement learning intrinsic motivation and self-supervised RL, together with Amy Zhang. Recording and slides are available on the tutorial website.
Jun 11, 2025	I gave a tutorial on intrinsic motivation and self-supervised RL at RLDM! Recording and slides are available on the tutorial website.
Apr 24, 2025	Princeton RL @ ICLR 2025! Some say hi in Singapore! A Single Goal is All You Need: Skills and Exploration Emerge from Contrastive RL without Rewards, Demonstrations, or Subgoals. Led by Grace Liu and Michael Tang. Accelerating Goal-Conditioned Reinforcement Learning Algorithms and Research. Led by Michał Bortkiewicz and lots of amazing co-authors. Invariance to Planning in Goal-Conditioned RL. Led by Cathy Ji and Vivek Myers. The “Law’’ of the Unconscious Contrastive Learner: Probabilistic Alignment of Unpaired Modalities. Led by Yongwei Che. Can a MISL Fly? Analysis and Ingredients for Mutual Information Skill Learning. Led by Chongyi Zheng and Jens Tuyls. OGBench: Benchmarking Offline Goal-Conditioned RL. Led by Seohong Park and Kevin Frans.
Jan 7, 2025	Awarded a grant from the Princeton AI Lab to study ``Do brains perceive, act, and plan using temporal contrast?’’ together with Nathaniel Daw.
Aug 9, 2024	In attempts to change perceptions about who does RL, we’ve put together a poster of Notable Women in RL!