arXiv:2605.21557v1 Announce Type: cross Abstract: Conventional wisdom holds that large-batch training is fundamentally incompatible with Reinforcement Learning (RL) - beyond a modest threshold, increasing batch sizes typically yields diminishing returns or performance degradation due to the inherent non-stationarity of the data distribution. We challenge this view by observing that non-stationarity is not a fixed property of RL, but evolves throughout training: early stages exhibit rapid behavioral shifts that demand small batches for plasticity, whereas late stages approach a quasi-stationary
The paper addresses a fundamental limitation in reinforcement learning (RL) optimization, offering a new approach to scalability that aligns with the increasing demands for advanced AI capabilities.
Improved scalability in RL training directly translates to more complex and capable AI systems, impacting fields from autonomous agents to enterprise automation.
The conventional understanding of large-batch training incompatibility with RL is challenged, potentially leading to more efficient and powerful RL model development.
- · AI developers
- · Reinforcement Learning researchers
- · Cloud computing providers
- · AI-driven industries
- · Inefficient RL training methodologies
- · Companies relying on outdated RL optimization techniques
More powerful and robust reinforcement learning models become feasible for complex tasks.
Accelerated development and deployment of sophisticated AI agents across various sectors.
Increased demand for specialized compute resources capable of handling large-scale RL training effectively.
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Read at arXiv cs.LG