Adaptive Inference Batching using Policy Gradients

2026-07-06 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Expert, medium

Summary

Adaptive Inference Batching using Policy Gradients explores reinforcement learning (RL) for dynamic batching and routing in inference serving systems, aiming to overcome limitations of static policies. Researchers trained REINFORCE and PPO agents on a simulator validated against queuing theory and production traces like Azure Functions and BurstGPT. The study found that in single-GPU scenarios with Poisson-like traffic, RL provides only marginal gains of +0.1% to +1.0% over well-tuned static batching. However, for multi-GPU heterogeneous routing, the RL agent developed a workload-segregation policy. This policy significantly improved performance, achieving a 3.5x (348%) gain over Round-Robin and 48% over the Shortest-Queue heuristic, alongside 60% higher throughput and 25% lower latency, all while meeting SLA constraints. The learned policy generalized effectively to unseen bursty and real-world traffic, and an attention-augmented network converged 20% faster.

Key takeaway

For MLOps Engineers optimizing multi-GPU inference serving, you should prioritize reinforcement learning for routing heterogeneous workloads. This approach can yield substantial performance gains, such as 60% higher throughput and 25% lower latency, by eliminating Head-of-Line blocking. Conversely, for single-GPU systems with predictable traffic, your existing well-tuned static batching policies are likely sufficient, as RL offers only marginal improvements. Focus RL efforts where combinatorial complexity truly benefits from adaptive, learned policies.

Key insights

Reinforcement learning significantly improves inference batching and routing for complex multi-GPU, heterogeneous workloads, but offers minimal gains for simpler single-GPU scenarios.

Principles

• Static batching is near-optimal for single-GPU, Poisson-like arrivals.
• RL's advantage concentrates in combinatorial, multi-resource decisions.
• Workload segregation policies eliminate Head-of-Line blocking.

Method

Train REINFORCE and PPO agents on a discrete-event simulator, modeling the problem as an MDP over queue state, request type, and GPU availability.

In practice

• Implement RL for multi-GPU routing to achieve 3.5x improvement over Round-Robin.
• Use attention-augmented policy networks for ~20% faster RL convergence.
• Prioritize RL for heterogeneous multi-resource scheduling over single-resource temporal scheduling.

Topics

• Adaptive Batching
• Reinforcement Learning
• Multi-GPU Inference
• Policy Gradients
• Head-of-Line Blocking
• Inference Serving

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

Related on AIssential

• Rewriting model inference with CUDA kernels: the bottleneck was not just GEMM [P] — Small-batch ML inference bottlenecks often stem from runtime glue, not just GEMM, requiring direct kernel optimization.
• Continuous Batching: How to Keep Your GPU Actually Busy — Continuous batching dynamically optimizes LLM inference throughput by keeping GPUs fully utilized through iteration-level scheduling.
• Maximizing GPU Utilization: Heterogeneous Pipelines with Ray and Kubernetes — Ray optimizes heterogeneous, distributed AI workloads by managing diverse compute resources and handling failures across complex, multi-layered stacks.
• Scalable Reinforcement Learning via Adaptive Batch Scaling — RL non-stationarity evolves, requiring adaptive batch scaling for optimal performance and model scaling.
• Scaling Agentic Inference Across Heterogeneous Compute with Zain Asgar - #757 — Heterogeneous AI inference across diverse hardware optimizes cost and performance for token-intensive agentic workloads.
• RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts — Optimizing MoE inference requires dynamic kernel configuration based on runtime expert routing, not just batch size.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.