arXiv:2510.19528v2 Announce Type: replace-cross Abstract: We investigate the fundamental problem of leveraging offline data to accelerate online reinforcement learning - a direction with strong potential but limited theoretical grounding. Our study centers on how to \emph{learn} and \emph{apply} value envelopes within this context. To this end, we introduce a principled two-stage framework: the first stage uses offline data to derive upper and lower bounds on value functions, while the second incorporates these learned bounds into online algorithms. Our method extends prior work by decoupling
This paper addresses a fundamental challenge in AI development by providing theoretical grounding for accelerating online reinforcement learning using offline data, a problem becoming more critical as access to real-world interaction becomes a bottleneck.
Improved methods for training AI systems using combined offline and online data can lead to more efficient, robust, and safer AI agents, accelerating their deployment in complex real-world scenarios.
The proposed two-stage framework offers a principled approach to integrating offline knowledge into online RL, potentially enabling faster convergence and better performance in agent training.
- · AI development platforms
- · Robotics companies
- · Logistics and automation sector
- · Research institutions in AI
- · Companies reliant on purely data-intensive, slow online RL
- · AI approaches that cannot leverage offline data effectively
More efficient and cost-effective development of advanced AI agents, particularly in domains where online interaction is expensive or risky.
Accelerated adoption of AI agents in various industries due to improved reliability and faster deployment cycles.
Enhanced AI capabilities contributing to the broader 'AI agents' narrative as more complex tasks become automatable with less data.
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