arXiv:2605.29405v1 Announce Type: new Abstract: Decision-making from offline datasets typically warm-starts a policy or score model from fixed offline data and then refines it with limited online interaction. Offline data reduces uncertainty, but it does not remove the need for exploration; it changes what remains to be explored. We formalise this residual uncertainty by the conditional mutual information $I(\chi;\tau_{1:T}\mid\mathcal{D}_N)$ between a learning target $\chi$ and the online trajectories after conditioning on the offline dataset. This view leads naturally to information-directed
The paper addresses a critical challenge in AI development by seeking to bridge the gap between fixed offline data training and real-world deployment with limited online interaction, driven by the increasing complexity and data demands of advanced AI systems.
This research provides a formal framework for optimizing resource-intensive online exploration in reinforcement learning, potentially leading to more efficient and robust AI systems across various applications.
The proposed 'information-directed' approach fundamentally alters how AI models transition from offline training to online refinement, emphasizing targeted exploration based on residual uncertainty.
- · AI developers
- · Robotics companies
- · Autonomous systems sector
- · Data-intensive industries
- · Companies with inefficient online learning paradigms
- · Brute-force exploration methods
More efficient and safer deployment of AI in complex, real-world environments.
Accelerated development cycles for AI-driven products requiring significant interaction with novel situations.
Potentially democratizes advanced AI deployment by reducing the need for extensive, costly online data collection.
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Read at arXiv cs.LG