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Source note3 min read

Adaptive Inference Batching using Policy Gradients

Source

arXiv

arxiv.org/abs/2607.05272v1

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Source

Plain-Language Brief

The paper investigates using reinforcement learning (RL) to create adaptive batching and routing policies for inference serving systems that can handle bursty, heterogeneous workloads. The research finds that in single-GPU settings, RL offers only marginal gains over well-tuned static batching policies under Poisson-like arrivals. However, in multi-GPU heterogeneous routing where fast and slow requests compete for shared resources, the agent discovers a workload-segregation policy that significantly improves throughput and latency compared to Round-Robin and other heuristics. The policy generalizes to unseen bursty and real-world traffic.

What the Document Says

  • The paper investigates using reinforcement learning for adaptive batching and routing policies in inference serving systems.
  • The research is conducted on a discrete-event simulator validated against queuing theory and production traces (Azure Functions, BurstGPT).
  • The problem is formulated as an MDP over queue state, request type, and GPU availability.
  • Evaluations are across standard Poisson traffic, extreme bursts, real-world traces, and heterogeneous multi-GPU routing.
  • In single-GPU settings, RL offers only marginal gains over well-tuned static batching policies under Poisson-like arrivals.
  • In multi-GPU heterogeneous routing, the agent discovers a workload-segregation policy that improves throughput and latency significantly compared to Round-Robin and other heuristics.
  • The policy generalizes to unseen bursty and real-world traffic despite training only on synthetic Poisson arrivals.
  • An attention-augmented policy network converges roughly 20% faster than an MLP baseline.

Key Concepts Mentioned

  • reinforcement learning
  • adaptive batching
  • inference serving systems
  • queuing theory
  • production traces
  • MDP
  • GPU availability
  • Poisson traffic
  • extreme bursts
  • heterogeneous multi-GPU routing
  • workload-segregation policy

Claims Made by the Document

  • RL can learn adaptive batching and routing policies that outperform heuristics.
  • In single-GPU settings, a well-tuned static batching policy is near-optimal under Poisson-like arrivals.
  • RL offers only marginal gains in single-GPU settings under Poisson-like arrivals.
  • In multi-GPU heterogeneous routing, the agent discovers a workload-segregation policy that improves throughput and latency significantly.
  • The policy generalizes to unseen bursty and real-world traffic.

Evidence Used

  • training REINFORCE and PPO agents on a discrete-event simulator
  • evaluations across standard Poisson traffic, extreme bursts, real-world traces, and heterogeneous multi-GPU routing

Methods / Approach

  • reinforcement learning algorithms (REINFORCE, PPO)
  • discrete-event simulation

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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 theory and production traces (Azure Functions, BurstGPT). We formulate the problem as an MDP over queue state, request type and GPU availability, evaluating across standard Poisson traffic, extreme bursts, real-world traces and heterogeneous multi-GPU routing. Our central finding is a clear boundary condition for RL's value in systems problems. In single-GPU settings, a well-tuned static batching policy is already near-optimal under Poisson-like arrivals and RL offers only marginal gains (+0.1% to +1.0%). In multi-GPU heterogeneous routing, however, where fast and slow requests compete for shared resources, the agent discovers a workload-segregation policy that eliminates Head-of-Line blocking, yielding a 3.5x (348%) improvement over Round-Robin and a 48% improvement over the strongest heuristic baseline (Shortest-Queue), with 60% higher throughput and 25% lower latency while respecting SLA constraints. The policy generalizes to unseen bursty and real-world traffic despite training only on synthetic Poisson arrivals and an attention-augmented policy network converges roughly 20% faster than an MLP baseline. These results suggest RL's advantage over engineered heuristics concentrates in combinatorial, multi-resource decisions rather than single-resource temporal scheduling, a practical distinction for deciding where learned policies justify their engineering cost in production inference infrastructure.