Scalable Reinforcement Learning via Adaptive Batch Scaling

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

Adaptive Batch Scaling (ABS) is a novel framework that challenges the conventional belief that large-batch training is incompatible with Reinforcement Learning (RL). ABS dynamically adjusts the effective batch size based on the evolving non-stationarity of the learning policy, quantified by "Behavioral Divergence," a new metric measuring action-level shifts between consecutive updates. This approach allows for small batches during early, volatile training stages to maintain plasticity and large batches in later, quasi-stationary stages for precise convergence. Integrated with the Parallelised Q-Network (PQN) algorithm and evaluated on the ALE benchmark, ABS significantly improves performance. It enables larger networks, such as PQN-L and PQN-XL, to achieve superior results with larger batch sizes, a scaling behavior previously considered unattainable in RL. The method also demonstrates generalizability to continuous control tasks with PPO and off-policy RL with BTR.

Key takeaway

For Machine Learning Engineers developing large-scale Reinforcement Learning agents, you should consider implementing Adaptive Batch Scaling (ABS) to overcome performance bottlenecks. By dynamically adjusting batch sizes based on policy stability, ABS enables your high-capacity models to leverage larger batches for stable convergence, a critical advantage previously limited to supervised learning. This approach can significantly improve sample efficiency and final performance across both on-policy and off-policy RL algorithms.

Key insights

RL non-stationarity evolves, requiring adaptive batch scaling for optimal performance and model scaling.

Principles

• RL non-stationarity is dynamic, not fixed.
• Small batches aid early-stage plasticity.
• Large batches ensure late-stage convergence.

Method

ABS dynamically adjusts rollout length (batch size) using "Behavioral Divergence," a forward-pass metric quantifying action-level policy shifts, to balance plasticity and convergence.

In practice

• Integrate ABS with PQN for Atari-57 performance gains.
• Apply ABS to PPO for continuous control tasks.
• Use ABS to scale large RL network architectures.

Topics

• Reinforcement Learning
• Adaptive Batch Scaling
• Behavioral Divergence
• On-Policy RL
• Large-Batch Training
• PQN Algorithm

Code references

• daisophila/ABS
• vwxyzjn/cleanrl

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.