New Multi-Play Bandit Framework Addresses Prioritized Resource Allocation for LLMs and Edge AI

Global: New Multi-Play Bandit Framework Addresses Prioritized Resource Allocation for LLMs and Edge AI

Researchers have introduced a variant of the multiple-play stochastic bandit problem that targets resource‑allocation challenges in large language model (LLM) deployments, edge intelligence, and related domains. The model features M distinct arms and K simultaneous plays, each play carrying a priority weight that influences how limited arm capacity is distributed.

Model Overview

In this formulation, every arm possesses a stochastic number of capacity units, and each unit is linked to a reward function. Plays compete for these units, and allocation proceeds in descending order of priority weight, ensuring that higher‑priority plays receive capacity first. This mechanism reflects real‑world scheduling scenarios where certain tasks must be prioritized over others.

Theoretical Guarantees

The authors derive instance‑independent and instance‑dependent lower bounds on regret, expressed as Ω(α₁σ√{KM T}) and Ω(α₁σ² M/Δ ln T) respectively, where α₁ denotes the largest priority weight, σ characterizes the reward tail, and Δ represents the minimum suboptimality gap. These bounds establish fundamental performance limits for any algorithm operating under the proposed model.

Algorithmic Solutions

To approach these limits, the paper presents an algorithm named MSB‑PRS‑OffOpt that identifies the optimal play‑allocation policy with computational complexity O(MK³). Building on this subroutine, the authors develop an approximate upper confidence bound (UCB) algorithm that attains regret upper bounds matching the lower bounds up to factors of √{K ln (KT)} for the instance‑independent case and α₁K² for the instance‑dependent case.

The development of these algorithms required addressing non‑trivial technical challenges associated with optimizing and learning under a nonlinear combinatorial utility function induced by the prioritized sharing mechanism. The authors detail how they overcame these obstacles through careful analysis of the utility structure and tailored confidence‑bound constructions.

Potential applications extend beyond LLM scheduling to include edge‑computing resource distribution, cloud‑service load balancing, and any setting where multiple agents vie for limited, stochastic resources under a hierarchy of priorities.

Future research directions suggested by the authors include extending the framework to dynamic priority adjustments, incorporating contextual information about arms, and evaluating empirical performance on real‑world workloads.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.