Adaptive Inference Batching using Policy Gradients

📰 ArXiv cs.AI

Learn to optimize inference batching using policy gradients for better throughput and latency in AI systems

advanced Published 7 Jul 2026

Action Steps

Implement a discrete-event simulator to model the inference serving system
Train a REINFORCE agent using policy gradients to learn adaptive batching policies
Compare the performance of the learned policy with static batching policies
Use PPO agents to further improve the adaptive batching and routing policies
Deploy the optimized policy in a real-world inference serving system

Who Needs to Know This

AI engineers and researchers can benefit from this technique to improve the efficiency of their inference serving systems, while data scientists can apply the reinforcement learning approach to other optimization problems

Key Insight

💡 Reinforcement learning can be used to learn adaptive batching and routing policies that outperform static heuristics in inference serving systems

Key Takeaways

Learn to optimize inference batching using policy gradients for better throughput and latency in AI systems

Full Article

Title: Adaptive Inference Batching using Policy Gradients

Abstract:
arXiv:2607.05272v1 Announce Type: cross Abstract: Inference serving systems must balance throughput and latency under bursty, heterogeneous workloads, yet the industry standard remains static batching policies that require manual tuning and cannot adapt to shifting traffic. We investigate whether reinforcement learning (RL) can learn adaptive batching and routing policies that outperform these heuristics, training REINFORCE and PPO agents on a discrete-event simulator validated against queuing t

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Adaptive Inference Batching using Policy Gradients

Key Takeaways

Full Article

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