4 papers
Rotate the Loop: Blockwise Conformality for Looped Transformers
Alexey Zemtsov, Albina Klepach, Vadim Lomshakov +5
Looped transformers reuse one shared block across many recurrent steps, so depth becomes a quantity that can be spent at runtime rather than a stack of distinct layers. Those extra…
Dual Advantage Fields
Alexey Zemtsov, Maxim Bobrin, Alexander Nikulin +5
Offline goal-conditioned reinforcement learning requires both long-horizon reachability estimates and local action comparisons. Dual goal representations provide value fields that…
Vintix II: Decision Pre-Trained Transformer is a Scalable In-Context Reinforcement Learner
Andrei Polubarov, Lyubaykin Nikita, Alexander Derevyagin +11
Recent progress in in-context reinforcement learning (ICRL) has demonstrated its potential for training generalist agents that can acquire new tasks directly at inference. Algorith…
XLand-100B: A Large-Scale Multi-Task Dataset for In-Context Reinforcement Learning
Alexander Nikulin, Ilya Zisman, Alexey Zemtsov +1
Following the success of the in-context learning paradigm in large-scale language and computer vision models, the recently emerging field of in-context reinforcement learning is ex…