AI RESEARCH
TabQL: In-Context Q-Learning with Tabular Foundation Models
arXiv CS.LG
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ArXi:2605.18979v1 Announce Type: new We propose Tabular Q-Learning (TabQL), a reinforcement learning framework that replaces the conventional parametric Q-network in Deep Q-Learning (DQN) with a tabular foundation model endowed with in-context learning capabilities. The key idea is to represent Q-values through a sequence-to-sequence foundation model operating over a tabularized representation of state-action-Q-value tuples, enabling rapid adaptation from limited online interaction by conditioning on recent experience.