arXiv:2605.27877v1 Announce Type: new Abstract: Offline policy improvement faces an inherent conflict between maximizing value and fitting the data distribution. While in-sample weighted regression is stable, it suffers from over-conservatism that suppresses high-value actions in the distribution tail; conversely, gradient-based approaches often exhibit a fitting-optimization conflict of gradients, which drives the policy off the data manifold. To address this, we propose Support-Preserving Action Rectification (SPAR), which reframes global learning as a local residual rectification anchored t
The paper addresses a core challenge in offline reinforcement learning, a field gaining traction for its potential to leverage existing datasets for policy improvement, aligning with current AI research trends.
Improving offline policy learning directly enhances the practicality and safety of deploying AI agents in real-world scenarios by making them more robust and less prone to 'off-manifold' actions.
The proposed SPAR method offers a more stable and effective way for AI systems to learn optimal policies from fixed datasets, potentially accelerating the development and deployment of advanced AI agents.
- · AI researchers
- · Robotics companies
- · Autonomous systems developers
- · Reinforcement learning platforms
- · Traditional offline RL methods
- · Systems highly sensitive to out-of-distribution actions
More reliable AI models developed from existing data without costly online interaction.
Faster and safer deployment of AI agents in critical applications like autonomous driving or industrial control.
Enhanced overall capability and reduced training costs for complex AI systems, leading to broader adoption across industries.
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Read at arXiv cs.LG