TabQL is a reinforcement learning framework that substitutes a tabular foundation model with in-context capabilities for the parametric Q-network in DQN, with a warm-up phase and theoretical analysis claiming improved sample efficiency.
On the power of foundation models
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
C3PO is a foundation model for bilevel pricing optimization that trains on simulated discrete choice data and retrieves elasticity priors from literature to improve revenue KPIs under business constraints.
citing papers explorer
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TabQL: In-Context Q-Learning with Tabular Foundation Models
TabQL is a reinforcement learning framework that substitutes a tabular foundation model with in-context capabilities for the parametric Q-network in DQN, with a warm-up phase and theoretical analysis claiming improved sample efficiency.
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Causal-Aware Foundation-Model for Bilevel Optimization in Discrete Choice Settings
C3PO is a foundation model for bilevel pricing optimization that trains on simulated discrete choice data and retrieves elasticity priors from literature to improve revenue KPIs under business constraints.