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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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Plain-Language Brief

The research proposes using reinforcement learning (RL) to create adaptive batching and routing policies for inference serving systems, which can handle bursty, heterogeneous workloads more efficiently than traditional static batching policies. The study finds that RL offers marginal gains in single-GPU settings but significant improvements in multi-GPU settings with competing fast and slow requests. The learned policy improves throughput by 60% and reduces latency by 25%, generalizing to unseen bursty and real-world traffic.

What the Document Says

  • The research investigates whether reinforcement learning can improve adaptive batching and routing policies for inference serving systems.
  • The study trains REINFORCE and PPO agents 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 conducted across standard Poisson traffic, extreme bursts, real-world traces, and heterogeneous multi-GPU routing.
  • In single-GPU settings, a well-tuned static batching policy is near-optimal under Poisson-like arrivals, and RL offers marginal gains (+0.1% to +1.0%).
  • In multi-GPU heterogeneous routing, the agent discovers a workload-segregation policy that eliminates Head-of-Line blocking, improving throughput by 60% and reducing latency by 25% while respecting SLA constraints.
  • 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.
  • The results suggest RL's advantage over engineered heuristics concentrates in combinatorial, multi-resource decisions rather than single-resource temporal scheduling.

Key Concepts Mentioned

  • reinforcement learning
  • adaptive batching
  • routing policies
  • inference serving systems
  • bursty workloads
  • heterogeneous workloads
  • discrete-event simulator
  • Queuing theory
  • production traces
  • Azure Functions
  • BurstGPT
  • MDP
  • queue state
  • request type
  • GPU availability
  • Poisson traffic
  • extreme bursts
  • Head-of-Line blocking
  • SLA constraints
  • attention-augmented policy network
  • MLP baseline

Claims Made by the Document

  • RL can learn adaptive batching and routing policies that outperform traditional heuristics.
  • In multi-GPU heterogeneous routing, the learned policy improves throughput by 60% and reduces latency by 25% compared to Round-Robin and the strongest heuristic baseline (Shortest-Queue).
  • The policy generalizes to unseen bursty and real-world traffic.
  • RL's advantage over engineered heuristics concentrates in combinatorial, multi-resource decisions rather than single-resource temporal scheduling.

Evidence Used

  • Results from training REINFORCE and PPO agents on a discrete-event simulator.
  • Evaluations across various traffic patterns and routing scenarios.
  • Comparisons with well-tuned static batching policies, Round-Robin, Shortest-Queue, and MLP baseline.

Methods / Approach

UNKNOWN

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.