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
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
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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
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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