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 theory and production traces (Azure Functions, BurstGPT). We formulate the problem as an MDP over que
The increasing complexity and scale of AI workloads, coupled with the need for efficiency and adaptiveness in AI inference serving, necessitate advanced solutions beyond static batching policies.
Adaptive batching and routing policies, driven by reinforcement learning, can significantly improve the cost-efficiency and performance of large-scale AI infrastructure, impacting both cloud providers and AI developers.
AI inference serving systems can move from manually tuned, static configurations to dynamic, self-optimizing systems, leading to better resource utilization and lower operational costs.
- · Cloud AI providers
- · Large language model developers
- · AI infrastructure software vendors
- · Legacy inference serving solutions
- · Organizations with static AI deployment strategies
Adaptive inference batching will reduce the operational expenditure for serving AI models, making AI more accessible.
Improved efficiency could accelerate the deployment of more complex and intelligent AI agents and applications across various industries.
Lower compute costs could exacerbate the energy demands of the overall AI ecosystem, despite per-unit efficiency gains.
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Read at arXiv cs.AI