Authors: Andrey Polubarov, Nikita Lyubaykin, Alexander Derevyagin, Ilya Zisman, Denis Tarasov, Alexander Nikulin, Vladislav Kurenkov
Abstract: 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 ICRL beyond toy tasks and single-domain settings remains an open challenge.
In this work, we present the first steps toward scaling ICRL by introducing a
fixed, cross-domain model capable of learning behaviors through in-context
reinforcement learning. Our results demonstrate that Algorithm Distillation, a
framework designed to facilitate ICRL, offers a compelling and competitive
alternative to expert distillation to construct versatile action models. These
findings highlight the potential of ICRL as a scalable approach for generalist
decision-making systems. Code to be released at
https://github.com/dunnolab/vintix
Source: http://arxiv.org/abs/2501.19400v1
