arXiv Machine Learning

Chance-Constrained Personalized LLM Routing with Online Preference Learning

arXiv Machine Learning

Jun 5

Learning to Route LLMs from Implicit Cost-Performance Preferences via Meta-Learning

arXiv:2606. 06178v1 Announce Type: new Abstract: Large language models (LLMs) present a trade-off between performance and cost, where more powerful models incur greater expense.

By Jiahao Zeng, Ming Tang, Ningning Ding

llms reinforcement-learning

arXiv AI

Jul 28

WISERouter: LLM Routing with Workload Budget Constraint

arXiv:2607. 23765v1 Announce Type: cross Abstract: Large language models (LLMs) achieve impressive performance across multiple domains, but using the most capable model for every query is prohibitive at scale.

By Yifei Li, Zihui Gao, Laks V. S. Lakshmanan

llms reinforcement-learning

arXiv Machine Learning

Sep 10

Online Learning with LLM Experts from Limited Feedback

arXiv:2609.05820v1 Announce Type: new Abstract: We study adaptive routing of prompts to large language model (LLM) experts to maximize response quality in an online setting with limited feedback. We...

By Wang Wei, Soumyabrata Pal, Koyel Mukherjee, Franck Dernoncourt, Ryan A. Rossi, Branislav Kveton, Hoda Eldardiry

llms reinforcement-learning

Hugging Face Trending Papers

Aug 17

Coverage-Maximizing Multinomial Subset Routing under Operational Constraints

We introduce Multinomial Subset Routing (MSR), a new online routing framework over $K$ experts in which the learner keeps a multinomial routing policy instead of a deterministic subset of experts. At each round, the learner samples $M$ experts i.

reinforcement-learning

arXiv Machine Learning

Jun 30

Learning to Route and Schedule LLMs from User Retrials via Contextual Queueing Bandits

arXiv:2602. 02061v2 Announce Type: replace Abstract: Explosive demands for LLMs often cause user queries to accumulate in server queues, requiring efficient routing (query-LLM matching) and scheduling (query prioritization) mechanisms.

By Seoungbin Bae, Junyoung Son, Dabeen Lee

llms rag

arXiv AI

Jun 19

Cost-Optimal LLM Routing with Limited User Feedback under User Satisfaction Guarantees

arXiv:2606. 19376v1 Announce Type: cross Abstract: Inference costs for large language model (LLM) applications are rapidly growing, driven by surging demand and rising infrastructure cost.

By Herbert Woisetschl\"ager, Arastun Mammadli, Ryan Zhang, Shiqiang Wang

llms benchmarks

arXiv AI

Aug 18

Coverage-Maximizing Multinomial Subset Routing under Operational Constraints

arXiv:2608. 16375v1 Announce Type: cross Abstract: We introduce Multinomial Subset Routing (MSR), a new online routing framework over $K$ experts in which the learner keeps a multinomial routing policy instead of a deterministic subset of experts.

By Quan Zhou, Yiyan Huang

reinforcement-learning

arXiv AI

Jul 13

Correlation-Aware Contextual Bandits with Surrogate Rewards for LLM Routing

arXiv:2607. 09015v1 Announce Type: cross Abstract: We study contextual bandit problems with correlated arms and access to surrogate reward signals produced by a machine learning model, motivated by applications such as large language model (LLM) routing.

By Ajay Narayanan Sridhar, Ronak Singh, Mehrdad Mahdavi, Vijaykrishnan Narayanan

llms reinforcement-learning benchmarks

arXiv AI

Jun 17

Online LLM Selection via Constrained Bandits with Time-Varying Demand

arXiv:2606. 17489v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed in edge-cloud inference systems to handle diverse user tasks with heterogeneous accuracy, latency, and cost profiles.

By Yin Huang, Qingsong Liu, Jie Xu

llms reinforcement-learning benchmarks

arXiv AI

Jun 16

Policy Regret for Embedding Model Routing: Contextual Bandits with Low-Rank Experts

arXiv:2606. 14929v1 Announce Type: cross Abstract: Modern recommendation systems increasingly rely on dynamically routing diverse queries to multiple embedding models.

By Yan Dai, Negin Golrezaei, Patrick Jaillet

rag reinforcement-learning safety

Hugging Face Trending Papers

Sep 22

Efficient Cost-Aware LLM Evaluation via Bayesian Bandit Gittins Indices

Exhaustively evaluating every candidate LLM configuration on every benchmark item to identify a high-performing one is costly. We formulate configuration selection as a cost-aware Bayesian bandit prob...

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jun 18

RouteJudge: An Open Platform for Reproducible and Preference-Aware LLM Routing

arXiv:2606. 18774v1 Announce Type: new Abstract: We present RouteJudge, an online pairwise preference evaluation framework for LLM routing systems, with a public platform available at https://routejudge.

By Guannan Lai, Haoran Hu, Han-Jia Ye

llms benchmarks

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