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LLM Bandit: Cost-Efficient LLM Generation via Preference-Conditioned Dynamic Routing
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The rapid advancement in large language models (LLMs) has brought forth a diverse range of models with varying capabilities that excel in different tasks and domains. However, selecting the optimal LLM for user queries often involves a challenging trade-off between accuracy and cost, a problem exacerbated by the diverse demands of individual queries. In this work, we present a novel framework that formulates the LLM selection process as a multi-armed bandit problem, enabling dynamic and intelligent routing of queries to the most appropriate model. Our approach incorporates a preference-conditioned dynamic routing mechanism, allowing users to specify their preferences at inference time, thereby offering a customizable balance between performance and cost. Additionally, our selection policy is designed to generalize to unseen LLMs, ensuring adaptability to new models as they emerge. Experimental results demonstrate that our method achieves significant improvements in both accuracy and cost-effectiveness across various LLM platforms, showcasing the potential of our framework to adaptively optimize LLM selection in real-world scenarios.
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Cited by 8 Pith papers
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SCATE: Learning to Supervise Coding Agents for Cost-Effective Test Generation
A contextual-bandit supervisor learns to route coding agents among default generation, analysis-augmented generation, and stop, yielding large coverage gains over unsupervised agents and non-agentic LLM test generators.
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PyroDash: Cost-Efficient Token-Level Small-Large Language Model Collaborative Inference
PyroDash lets a small model decide mid-generation to hand off once to a frozen LLM, achieving 64.04% accuracy (+6.36 points vs LLM) at lambda=0.05 and 96.4% lower estimated cost at lambda=0.6.
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Replacing Parameters with Preferences: Federated Alignment of Heterogeneous Vision-Language Models
MoR lets clients train local reward models on private preferences and uses a learned Mixture-of-Rewards with GRPO on the server to align a shared base VLM without exchanging parameters, architectures, or raw data.
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Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges
A survey that builds a taxonomy of edge-cloud LLM-SLM collaboration for inference and training, claiming to be the first to unify both phases.
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A systematic survey of LLM ensemble methods organized into a taxonomy of ensemble-before-inference, ensemble-during-inference, and ensemble-after-inference stages, with review of benchmarks, applications, and future d...
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