arXiv:2602.02250v2 Announce Type: replace-cross Abstract: Kullback-Leibler (KL) divergence regularization is widely used in reinforcement learning, but it becomes infinite under support mismatch and can degenerate in low-noise regimes. Using a unified information-geometric framework, we introduce KL analogs by replacing the Fisher-Rao geometry in the dynamical formulation of the KL with transport-based geometries, and derive closed-form expressions for common distribution families. Between elliptic distributions, these divergences remain finite for degenerating equal covariances and yield a ge
Published in 2026, this research indicates ongoing advancements in the theoretical underpinnings of AI, addressing known limitations in current regularization techniques.
Improved KL-regularization methods could lead to more robust, stable, and efficient reinforcement learning algorithms, critical for complex AI systems and agents.
The development of novel KL divergence analogs that are finite in previously problematic low-noise regimes could significantly enhance the practical applicability of reinforcement learning.
- · AI researchers
- · Reinforcement learning applications
- · Autonomous systems developers
- · AI models constrained by divergence issues
More reliable training for reinforcement learning models, especially in scenarios with low noise or support mismatch.
Accelerated development and deployment of sophisticated AI agents due to enhanced learning stability.
Potentially broader commercial adoption of advanced AI systems that were previously impractical due to model instability.
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Read at arXiv cs.LG