Alexander Derevyagin — accepted AI-conference papers

2026

Decision Pre-Trained Transformer is a Scalable In-Context Reinforcement Learner

Andrei Polubarov, Lyubaykin Nikita, Alexander Derevyagin, Artyom Grishin, Igor Saprygin, Aleksandr Serkov +8

ICLR 2026poster

Recent progress in in-context reinforcement learning (ICRL) has demonstrated its potential for training generalist agents that can acquire new tasks directly at inference. Algorithm Distillation (AD) pioneered this paradigm and was subsequently scaled to multi-domain settings, although its ability t…

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2026

Object-Centric Latent Action Learning

Albina Klepach, Alexander Nikulin, Ilya Zisman, Denis Tarasov, Alexander Derevyagin, Andrei Polubarov +3

AAAI 2026technical

Leveraging vast amounts of unlabeled internet video data for embodied AI is currently bottlenecked by the lack of action labels and the presence of action-correlated visual distractors. Although recent latent action policy optimization (LAPO) has shown promise in inferring proxy action labels from v

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2025

Vintix: Action Model via In-Context Reinforcement Learning

Andrei Polubarov, Lyubaykin Nikita, Alexander Derevyagin, Ilya Zisman, Denis Tarasov, Alexander Nikulin +1

ICML 2025poster

In-Context Reinforcement Learning (ICRL) represents a promising paradigm for developing generalist agents that learn at inference time through trial-and-error interactions, analogous to how large language models adapt contextually, but with a focus on reward maximization. However, the scalability of…

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