Reward-Based Online LLM Routing via NeuralUCB

2026-03-31 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, quick

Summary

This study introduces a NeuralUCB-based routing policy for cost-aware large language model (LLM) routing, addressing limitations of existing supervised and partial-feedback methods. The proposed method was evaluated on RouterBench in a simulated online environment. Experimental results demonstrate that this NeuralUCB approach consistently surpasses random and min-cost baselines in utility reward. Furthermore, it achieves significantly lower inference costs compared to a max-quality reference while maintaining competitive reward performance. These findings indicate NeuralUCB's potential for efficient, cost-aware LLM routing, though challenges in action discrimination and exploration persist.

Key takeaway

For AI Architects optimizing LLM deployment costs, integrating NeuralUCB into your routing strategy could significantly reduce inference expenses without sacrificing performance. Your teams should investigate NeuralUCB's ability to balance utility reward and cost, particularly for online routing scenarios, to achieve more efficient resource utilization.

Key insights

NeuralUCB offers a promising approach for cost-aware LLM routing, balancing utility reward and inference cost.

Principles

• NeuralUCB can balance cost and quality.
• Online routing requires adaptive policies.

Method

The method implements a NeuralUCB-based routing policy, evaluating it on RouterBench in a simulated online setting to compare utility reward and inference cost against baselines.

In practice

• Implement NeuralUCB for LLM routing.
• Evaluate routing on RouterBench.

Topics

• LLM Routing
• NeuralUCB
• Cost-aware Routing
• Online Learning
• RouterBench

Best for: AI Architect, NLP Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.