Announcing the RLC 2026 Outstanding Paper Awards
We are honoured to announce the winners of the Outstanding Paper Awards at the Third Reinforcement Learning Conference. Papers are awarded based on specific aspects of their contribution.
This year's awards consist of seven papers. The awarded papers are listed below in alphabetical order by award name. Congratulations to all the authors!
Applications of Reinforcement Learning
Discovering High Quality Chess Puzzles with Offline Reinforcement Learning
Authors: Allen Nie, Anirudhan Badrinath, Nicholas Tomlin, Timothy Dai, Carissa Yip, Rose E Wang, Emma Brunskill, and Christopher J Piech
Dynamics Models for Offline Hyperparameter Selection in Real-World RL
Authors: Jordan Coblin, Han Wang, Martha White, and Adam White
Emerging Topics in Reinforcement Learning
Yes, Q-learning Helps Offline In-Context RL
Authors: Denis Tarasov, Alexander Nikulin, Ilya Zisman, Albina Klepach, Andrei Polubarov, Lyubaykin Nikita, Alexander Derevyagin, Igor Kiselev, and Vladislav Kurenkov
Empirical Reinforcement Learning Research
Gradient Iterated Temporal-Difference Learning
Authors: Théo Vincent, Kevin Gerhardt, Yogesh Tripathi, Habib Maraqten, Adam White, Martha White, Jan Peters, and Carlo D'Eramo
Resourcefulness in Reinforcement Learning
Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning
Authors: Jiaheng Hu, Jay Shim, Chen Tang, Yoonchang Sung, Bo Liu, Peter Stone, and Roberto Martín-Martín
Scientific Understanding in Reinforcement Learning
Minimal Ingredients for Reward Assignment from Expert Demonstrations
Authors: Zixuan Dong, Yumi Omori, and Keith W. Ross
Tooling, Environments, and Evaluation for Reinforcement Learning
Assistax: A Multi-Agent Hardware-Accelerated Reinforcement Learning Benchmark for Assistive Robotics
Authors: Leonard Hinckeldey, Elliot Fosong, Rimvydas Rubavicius, Elle Miller, Trevor McInroe, Fan Zhang, Patricia Wollstadt, Stefano V. Albrecht, and Subramanian Ramamoorthy