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New LLM routing policy maximizes task completion within fixed budgets

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new contextual bandit policy called ADP-HT designed to optimize the allocation of tasks to large language models (LLMs) within fixed budgets. This policy learns task-specific success probabilities and estimates costs using coarsened response-length labels, aiming to maximize the number of tasks solved before a budget is exhausted. ADP-HT demonstrated superior performance on benchmarks like MATH500, GPQA, RouterBench, and LiveCodeBench, outperforming existing baselines and even a deployed commercial router under tight budget constraints. AI

IMPACT Optimizes LLM usage and cost-efficiency for organizations operating under budget constraints.

RANK_REASON Research paper detailing a new algorithm for LLM routing. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New LLM routing policy maximizes task completion within fixed budgets

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Research paper detailing a new algorithm for LLM routing. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Hanzhang Qin, Sophie H. Yu, Yuewei Zhang, Zhi-Hai Zhang ·

    Pathwise-Budgeted LLM Routing: Online Allocation with K-Bit Tail-Cost Learning

    arXiv:2611.05835v1 Announce Type: new Abstract: Firms using large language models under fixed budgets must decide which model to call and whether to retry a failed task. Because a call's charge is revealed only after completion, one long response can exhaust the remaining budget …

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