Reinforcement Learning Foundation Models Should Already Be A Thing
Abdelrahman Zighem, Jill-J\^enn Vie
Why It Matters
What makes this one worth your time
This work suggests a new direction for reinforcement learning by leveraging synthetic data and attention-based architectures, potentially simplifying the development of RL models.
Proposes reinforcement learning foundation models using synthetic MDPs and demonstrates their potential with a Graph Attention Network.
Summary
The paper proposes the concept of reinforcement learning foundation models using synthetic MDPs and demonstrates a proof of concept with a Graph Attention Network that solves tabular benchmarks efficiently.
Key contributions
- Introduces the idea of RL foundation models using synthetic MDPs.
- Demonstrates a Graph Attention Network trained on synthetic MDPs that performs well on tabular benchmarks.
Notable insights
- MDPs can be represented with a fixed-size sufficient statistic, making them suitable for attention-based architectures.
- Synthetic MDPs can be used to train RL models without task-specific tuning, achieving competitive performance.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2606.18812v2 Announce Type: replace-cross Abstract: Foundation models for language and vision are powered by internet-scale data, while structured domains such as tabular prediction are powered by synthetic data. This substitute shifts the challenge from collection to prior design. Such priors already exist for many structured tasks: TabPFN and its successors solve tabular classification with a transformer pretrained on a synthetic Bayesian prior. We make two points. \textbf{First}, reinforcement learning is the conspicuous gap: sampling a synthetic MDP is as feasible as sampling a synthetic tabular dataset, yet no in-context RL work treats prior design as a primary objective. \textbf{Second}, MDPs admit a fixed-size sufficient statistic, independent of the episodes observed and tabular in shape, which makes them directly amenable to the attention-based architectures used for tabular foundation models, with a policy head replacing the supervised target. Together these define the agenda for an RL foundation model. As a proof of concept, we train a Graph Attention Network entirely on synthetic MDPs and show that, with no task-specific tuning, it solves held-out tabular benchmarks in context, both online and offline: online, in far fewer episodes than UCB-VI and tabular Q-learning, and offline, competitively with VI-LCB.