Researchers have developed a novel approach to large language model routing using NeuralUCB, a cost-aware algorithm that balances efficiency and adaptivity. This method improves upon existing supervised and partial-feedback routing techniques, which often trade off one aspect for the other. By implementing a NeuralUCB-based routing policy and testing it on RouterBench, the study demonstrates the potential for more effective online routing. The experimental results show promise for this approach, which could have significant implications for the field of natural language processing. The use of NeuralUCB allows for a more nuanced understanding of the tradeoffs involved in LLM routing, enabling more informed decisions about resource allocation1. This matters to practitioners because it could lead to more efficient and effective use of large language models, with potential applications in a range of fields, from chatbots to language translation systems, thereby enhancing the overall performance and reliability of these systems.
Reward-Based Online LLM Routing via NeuralUCB
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References
- arXiv. (2026, March 31). Reward-Based Online LLM Routing via NeuralUCB. *arXiv*. https://arxiv.org/abs/2603.30035v1
Original Source
arXiv ML
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