OrcaRouter: A Production-Oriented LLM Router with Hybrid Offline-Online Learning
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Computer Science > Machine Learning
Title:OrcaRouter: A Production-Oriented LLM Router with Hybrid Offline-Online Learning
Abstract:The rapid development of large language models, each with distinct capabilities and inference costs, raises a practical deployment question: given an incoming request, which model should handle it? We present OrcaRouter, a production-oriented LLM router that combines a LinUCB-based contextual bandit over lexical and sentence-embedding features with a hybrid offline-online learning protocol. Offline, OrcaRouter obtains full-information feedback by evaluating each candidate model on a curated set of routing prompts, yielding a reward matrix used to fit one ridge regressor per arm. At deployment time, it initializes from these parameters and can optionally continue learning from bandit feedback, updating only the selected model's arm after observing its reward. At the time of our RouterArena submission (May 20, 2026), OrcaRouter-Adaptive ranked second on the public RouterArena leaderboard with an arena score of 72.08, achieving 75.54% accuracy at a cost of USD 1.00 per 1,000 queries.
| Comments: | 6 pages, 1 table. Technical report |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.30736 [cs.LG] |
| (or arXiv:2605.30736v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.30736
arXiv-issued DOI via DataCite (pending registration)
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