Accepted Papers

Area Chairs

• Theory, Robotics, and Language Modeling: Bhavya Sukhija, Andrew Wagenmaker, Parnian Kassraie, Lenart Treven, Carmelo Sferrazza
• AI for Science: Vignesh Ram Somnath, Mojmír Mutný

Best Papers

Best papers are awarded 500USD and assigned a short presentation slot.

Accepted Papers

RL and Theory

• The Effective Horizon Challenge, Cassidy Laidlaw, Daniel Khalil, Michelle Li, Laker Newhouse, Stuart Russell, Anca Dragan.
• No-Regret Safety: Balancing Tests and Misclassification in Logistic Bandits, Tavor Baharav, Spyros Dragazis, Aldo Pacchiano.
• Provably Learning from Language Feedback, Wanqiao Xu, Allen Nie, Ruijie Zheng, Aditya Modi, Adith Swaminathan, Ching-An Cheng.
• Retrospective and Structurally Informed Exploration via Cross-task Successor Feature Similarity, Arya Ebrahimi, Jun Jin.
• Greed is Good: A Unifying Perspective on Guided Generation, Zander W. Blasingame, Chen Liu.
• Intrinsic Benefits of Categorical Distributional Loss: Uncertainty-aware Regularized Exploration in Reinforcement Learning, Ke Sun, Yingnan Zhao, Enze Shi, Yafei Wang, Xiaodong Yan, Bei Jiang, Linglong Kong.
• Sparse Optimistic Information Directed Sampling, Ludovic Schwartz, Hamish Flynn, Gergely Neu.
• In-Context Learning for Pure Exploration, Alessio Russo, Ryan Welch, Aldo Pacchiano.
• Distances for Markov chains from sample streams, Sergio Calo, Anders Jonsson, Gergely Neu, Ludovic Schwartz, Javier Segovia-Aguas.
• Oracle-Efficient Adversarial Reinforcement Learning via Max-Following, Sikata Bela Sengupta, Zakaria Mhammedi, Teodor Vanislavov Marinov.
• Prompts Generalize with Low Data: Non-vacuous Generalization Bounds for Optimizing Prompts with More Informative Priors, Qiuyi Zhang, David Madras, Joshua Safyan.
• Towards Unsupervised Multi-Agent Reinforcement Learning via Task-Agnostic Exploration, Riccardo Zamboni, Mirco Mutti, Marcello Restelli.
• Instance-Dependent Fixed-Budget Pure Exploration in Reinforcement Learning, Yeongjong Kim, Yeoneung Kim, Kwang-Sung Jun.
• Reinforcement Learning with Thompson Sampling: No-Regret Performance over Finite Horizons, Jasmine Bayrooti, Sattar Vakili, Amanda Prorok, Carl Henrik Ek.

Language Modeling

• Fleet of Agents: Coordinated Problem Solving with Large Language Models, Lars Henning Klein, Nearchos Potamitis, Roland Aydin, Robert West, Caglar Gulcehre, Akhil Arora.
• G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning, Xiaojun Guo, Ang Li, Yifei Wang, Stefanie Jegelka, Yisen Wang.
• Intent Factored Generation: Unleashing the Diversity in Your Language Model, Eltayeb Ahmed, Uljad Berdica, Martha Elliott, Danijela Horak, Jakob Nicolaus Foerster.
• From Words to Rewards: Leveraging Natural Language for Reinforcement Learning, Belen Martin Urcelay, Andreas Krause, Giorgia Ramponi.
• Think or Not? Selective Reasoning via Reinforcement Learning for Vision-Language Models, Jiaqi WANG, Kevin Qinghong Lin, James Cheng, Mike Zheng Shou.
• Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM, Thang Duong, Minglai Yang, Chicheng Zhang.
• Llama-Nemotron: Efficient Reasoning Models, Soumye Singhal, Jiaqi Zeng, Alexander Bukharin, Yian Zhang, Gerald Shen, Ameya Sunil Mahabaleshwarkar, Bilal Kartal, Yoshi Suhara, Akhiad Bercovich, Itay Levy, Izik Golan, Mohammed Dabbah, Ran El-Yaniv, Somshubra Majumdar, Igor Gitman, Evelina Bakhturina, Jimmy J. Zhang, Bor-Yiing Su, Guyue Huang, Izzy Putterman, Mostofa Patwary, Oluwatobi Olabiyi, Olivier Delalleau, Bryan Catanzaro, Boris Ginsburg, Oleksii Kuchaiev, Tugrul Konuk.
• e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs, Amrith Setlur, Matthew Y. R. Yang, Charlie Victor Snell, Jeremiah Greer, Ian Wu, Virginia Smith, Max Simchowitz, Aviral Kumar.
• LLMs are Greedy Agents: Effects of RL Fine-tuning on Decision-Making Abilities, Thomas Schmied, Jörg Bornschein, Jordi Grau-Moya, Markus Wulfmeier, Razvan Pascanu.
• Gathering Context that Supports Decisions via Entropy Search with Language Models, Sang T. Truong, Sicong Huang, Pranava Singhal, Tai Dang, Yukang Wen, Duc Quang Nguyen, Violet Xiang, Sanmi Koyejo, Nick Haber.
• See it to Place it: Evolving Macro Placements with Vision Language Models, Ikechukwu Uchendu, Vincent Zhuang, Wenjie Jiang, Kuang-Huei Lee, Ebrahim Songhori, Swati Goel, Karly Hou, Vijay Janapa Reddi.
• Kevin: Multi-Turn RL for Generating CUDA Kernels, Carlo Baronio, Pietro Marsella, Ben Pan, Simon Guo, Silas Alberti.
• Toward Efficient Exploration by Large Language Model Agents, Dilip Arumugam, Thomas L. Griffiths.
• EVOLvE: Evaluating and Optimizing LLMs ForIn-Context Exploration, Allen Nie, Yi Su, Bo Chang, Jonathan Lee, Ed H. Chi, Quoc V Le, Minmin Chen.
• The Road Not Taken: Hindsight Exploration for LLMs in Multi-Turn RL, Huaxiaoyue Wang, Sanjiban Choudhury.

Robotics

• Blindfolded Experts Generalize Better: Insights from Robotic Manipulation and Videogames, Ev Zisselman, Mirco Mutti, Shelly Francis-Meretzki, Elisei Shafer, Aviv Tamar.
• Exploration by Exploitation: Curriculum Learning for Reinforcement Learning Agents through Competence-Based Curriculum Policy Search, Tabitha Edith Lee, Nan Rosemary Ke, Sarvesh Patil, Annya Dahmani, Eunice Yiu, Esra'a Saleh, Alison Gopnik, Oliver Kroemer, Glen Berseth.
• Active Advantage-Aligned Online Reinforcement Learning with Offline Data, Xuefeng Liu, Hung T. C. Le, Siyu Chen, Rick Stevens, Zhuoran Yang, Matthew Walter, Yuxin Chen.
• Diffusion-Based Maximum Entropy Reinforcement Learning, Onur Celik, Zechu Li, Denis Blessing, Ge Li, Daniel Palenicek, Jan Peters, Georgia Chalvatzaki, Gerhard Neumann.
• Direct Regret Optimization in Bayesian Optimization, Fengxue Zhang, Yuxin Chen.
• Strategic Vantage Selection for Learning Viewpoint-Agnostic Manipulation Policies, Sreevishakh Vasudevan, Som Sagar, Ransalu Senanayake.
• Scalable and Efficient Exploration via Intrinsic Rewards in Continuous-time Dynamical Systems, Klemens Iten, Andreas Krause.
• Sample-Efficient Reinforcement Learning with Action Chunking, Qiyang Li, Zhiyuan Zhou, Sergey Levine.
• Central Path Proximal Policy Optimization, Nikola Milosevic, Johannes Müller, Nico Scherf.
• DISCOVER: Automated Curricula for Sparse-Reward Reinforcement Learning, Leander Diaz-Bone, Marco Bagatella, Jonas Hübotter, Andreas Krause.

AI for Science

• SOAPIA: Siamese-Guided Generation of Off Target-Avoiding Protein Interactions with High Target Affinity, Sophia Vincoff, Oscar Davis, Yinuo Zhang, Ismail Ilkan Ceylan, Alexander Tong, Joey Bose, Pranam Chatterjee.
• Flow Density Control: Generative Optimization Beyond Entropy-Regularized Fine-Tuning, Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh, Zebang Shen, Niao He, Andreas Krause.
• Branched Schrödinger Bridge Matching, Sophia Tang, Yinuo Zhang, Alexander Tong, Pranam Chatterjee.
• Rethinking Exploration in Asynchronous Bayesian Optimization: Standard Acquisition is All You Need, Ben Riegler, James A C Odgers, Vincent Fortuin.
• Stabilizing protein fitness predictors via the PCS framework, Omer Ronen, Alex Y. Zhao, Ron Boger, Chengzhong Ye, Bin Yu.
• StemCell-GPT: A Specialized AI Agent For Human Stem Cell Engineering, Jingwen Hui, Freja Kjellaug Amalia Ekman, Hana Yousef Ghanim, Sridhar Selvaraj, Yuanhao Qu, Matthew Porteus, Le Cong.
• A Diffusion Model to Shrink Proteins While Maintaining their Function, Ethan Baron, Alan Nawzad Amin, Ruben Weitzman, Debora Susan Marks, Andrew Gordon Wilson.
• Bayesian Hypothesis Testing Policy Regularization, Sarah Rathnam, Susan Murphy, Finale Doshi-Velez.
• Automated Data Selection for Efficient Cost Model Training to Optimize Sparse Matrix Kernels on Emerging Hardware Accelerators, Chamika Sudusinghe, Gerasimos Gerogiannis, Damitha Lenadora, Charles Block, Josep Torrellas, Charith Mendis.
• Improved Exploration in GFlownets via Enhanced Epistemic Neural Networks, Sajan Muhammad, Salem Lahlou.
• Diversity By Design: Leveraging Distribution Matching for Offline Model-Based Optimization, Michael S Yao, James Gee, Osbert Bastani.
• Reimagining Parameter Space Exploration with Diffusion Models, Lijun Zhang, Xiao Liu, Hui Guan.
• Testing LLM Understanding of Scientific Literature through Expert-Driven Question Answering: Insights from High-Temperature Superconductivity, Haoyu Guo, Maria Tikhanovskaya, Paul Raccuglia, Alexey Vlaskin, Christopher Co, Daniel J. Liebling, Scott Ellsworth, Matthew Abraham, Elizabeth Dorfman, N.P. Armitage, John M. Tranquada, Senthil Todadri, Antoine Georges, Subir Sachdev, Steven Kivelson, B. J. Ramshaw, Chunhan Feng, Olivier Gingras, Vadim Oganesyan, Michael Brenner, Subhashini Venugopalan, Eun-Ah Kim.
• Align While Search: Belief-Guided Exploratory Inference for Test-Time World Alignment, Seohui Bae, Jeonghye Kim, Youngchul Sung, Woohyung Lim.