BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute

• URL: http://arxiv.org/abs/2506.22716v1
• Date: Sat, 28 Jun 2025 01:52:50 GMT
• Title: BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute
• Abstract summary: BEST-Route is a novel routing framework that chooses a model and the number of responses to sample from it based on query difficulty and the quality thresholds.<n> Experiments on real-world datasets demonstrate that our method reduces costs by up to 60% with less than 1% performance drop.
• Score: 25.740809143951815
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large language models (LLMs) are powerful tools but are often expensive to deploy at scale. LLM query routing mitigates this by dynamically assigning queries to models of varying cost and quality to obtain a desired trade-off. Prior query routing approaches generate only one response from the selected model and a single response from a small (inexpensive) model was often not good enough to beat a response from a large (expensive) model due to which they end up overusing the large model and missing out on potential cost savings. However, it is well known that for small models, generating multiple responses and selecting the best can enhance quality while remaining cheaper than a single large-model response. We leverage this idea to propose BEST-Route, a novel routing framework that chooses a model and the number of responses to sample from it based on query difficulty and the quality thresholds. Experiments on real-world datasets demonstrate that our method reduces costs by up to 60% with less than 1% performance drop.

Related papers

• Before Thinking, Learn to Decide: Proactive Routing for Efficient Visual Reasoning [87.18590662029432]
  We propose PRP, a Proactive Routing Paradigm that enables early decision-making by jointly evaluating the competence of both the draft and target models.<n>Our draft Rating Learning (DRL) equips the draft model with an internal confidence estimator, while Joint Rating Learning (JRL) predicts how well the target model can handle a given query.<n>These ratings enable fine-grained, instance-level textbfProactive Routing and substantially accelerate inference without compromising overall performance.
  arXiv  Detail & Related papers  (2026-06-29T12:30:24Z)
• Cluster, Route, Escalate: Cascaded Framework for Cost-Aware LLM Serving [69.5155152169752]
  We propose a two-stage cascaded solution for large language models (LLMs)<n>Stage 1 clusters incoming queries and assigns each cluster to its most cost-effective model.<n>Stage 2 adds a quality estimation (QE) cascade; when an output from Stage 1 is judged low-quality, the query is escalated to a stronger model.<n>On the test datasets, the cascaded system retains 97-99% of the strongest model's accuracy while reducing Time Per Output Token (TPOT)<n>It requires only task-correctness labels and adapts to changes in the model pool without manual reconfiguration.
  arXiv  Detail & Related papers  (2026-06-25T18:29:24Z)
• RouteLMT: Learned Sample Routing for Hybrid LLM Translation Deployment [57.588738943463646]
  Large Language Models (LLMs) have achieved remarkable performance in Machine Translation (MT)<n> deploying them at scale remains prohibitively expensive.<n>We propose textbfRouteLMT, an in-model router that predicts the expected gain by probing the small translators prompt-token representation.
  arXiv  Detail & Related papers  (2026-04-24T13:02:45Z)
• Models Under SCOPE: Scalable and Controllable Routing via Pre-hoc Reasoning [28.165465162107253]
  We propose SCOPE, a routing framework that goes beyond model selection by predicting their cost and performance.<n>SCOPE makes reasoning-based predictions by retrieving how models behave on similar problems, rather than relying on fixed model names.<n>It can boost accuracy by up to 25.7% when performance is the priority, or cut costs by up to 95.1% when efficiency matters most.
  arXiv  Detail & Related papers  (2026-01-29T21:09:36Z)
• RouteMoA: Dynamic Routing without Pre-Inference Boosts Efficient Mixture-of-Agents [91.0187958746262]
  RouteMoA is an efficient mixture-of-agents framework with dynamic routing.<n>It employs a lightweight scorer to perform initial screening by predicting coarse-grained performance from the query.<n>It refines these scores through lightweight self- and cross-assessment based on existing model outputs, providing posterior correction without additional inference.
  arXiv  Detail & Related papers  (2026-01-26T04:22:22Z)
• RoBoN: Routed Online Best-of-n for Test-Time Scaling with Multiple LLMs [0.0]
  Routed Online Best-of-$n$ is a sequential multi-LLM alternative to the prevailing single-model best-of-$n$.<n>Our results indicate that diversity across models can be exploited at inference to improve best-of-$n$ performance over any constituent model alone.
  arXiv  Detail & Related papers  (2025-12-05T08:55:39Z)
• ECVL-ROUTER: Scenario-Aware Routing for Vision-Language Models [26.059355108708374]
  We propose ECVL-ROUTER, the first scenario-aware routing framework for Vision-Language Models (VLMs)<n>Our approach introduces a new routing strategy and evaluation metrics that dynamically select the appropriate model for each query based on user requirements.<n>Results show that our approach successfully routes over 80% of queries to the small model while incurring less than 10% drop in problem solving probability.
  arXiv  Detail & Related papers  (2025-10-31T07:46:44Z)
• DISCO: Diversifying Sample Condensation for Efficient Model Evaluation [59.01400190971061]
  Costly evaluation reduces inclusivity, slows the cycle of innovation, and worsens environmental impact.<n>We argue that promoting diversity among samples is not essential; what matters is to select samples thatmaximise diversity in model responses.<n>Our method, $textbfDiversifying Sample Condensation (DISCO)$, selects the top-k samples with the greatest model disagreements.
  arXiv  Detail & Related papers  (2025-10-09T08:53:59Z)
• Learning to Route LLMs from Bandit Feedback: One Policy, Many Trade-offs [69.2486294522259]
  BaRP is a Bandit Routing-feedback with Preferences approach that trains under the same partial-feedback restriction as deployment.<n> Framed as a contextual bandit over prompt features and a user preference vector, our method simulates an online feedback setting during training and adapts its routing decisions to each new prompt.
  arXiv  Detail & Related papers  (2025-10-08T18:24:59Z)
• Aligning Frozen LLMs by Reinforcement Learning: An Iterative Reweight-then-Optimize Approach [65.6966065843227]
  Iterative Reweight-then-IRO is a framework that performs RL-style alignment of a frozen base model without touching its parameters.<n>At test time, the value functions are used to guide the base model generation via a search-based optimization process.<n> Notably, users can apply IRO to align a model on their own dataset, similar to OpenAI's reinforcement fine-tuning (RFT)
  arXiv  Detail & Related papers  (2025-06-21T21:49:02Z)
• Arch-Router: Aligning LLM Routing with Human Preferences [1.859931123372708]
  routing has become an essential technique to operationalize the use of different models.<n>We propose a preference-aligned routing framework that guides model selection by matching queries to user-defined domains.<n>Our approach captures subjective evaluation criteria and makes routing decisions more transparent and flexible.
  arXiv  Detail & Related papers  (2025-06-19T23:57:41Z)
• PromptWise: Online Learning for Cost-Aware Prompt Assignment in Generative Models [22.732551029493987]
  We introduce PromptWise, an online learning framework designed to assign a sequence of prompts to a group of large language models.<n> PromptWise strategically queries cheaper models first, progressing to more expensive options only if the lower-cost models fail to adequately address a given prompt.<n>The results highlight that PromptWise consistently outperforms cost-unaware baseline methods.
  arXiv  Detail & Related papers  (2025-05-24T23:26:33Z)
• OmniRouter: Budget and Performance Controllable Multi-LLM Routing [31.60019342381251]
  Large language models (LLMs) deliver superior performance but require substantial computational resources and operate with relatively low efficiency.<n>We introduce Omni, a controllable routing framework for multi-LLM serving.<n>Experiments show that Omni achieves up to 6.30% improvement in response accuracy while simultaneously reducing computational costs by at least 10.15%.
  arXiv  Detail & Related papers  (2025-02-27T22:35:31Z)
• MixLLM: Dynamic Routing in Mixed Large Language Models [57.309520357563215]
  Large Language Models (LLMs) exhibit potential artificial generic intelligence recently, however, their usage is costly with high response latency.<n>We develop MixLLM, a dynamic contextual-bandit-based routing system for query-LLM assignment.
  arXiv  Detail & Related papers  (2025-02-09T02:26:15Z)
• PickLLM: Context-Aware RL-Assisted Large Language Model Routing [0.5325390073522079]
  PickLLM is a lightweight framework that relies on Reinforcement Learning (RL) to route on-the-fly queries to available models.<n>We demonstrate the speed of convergence for different learning rates and improvement in hard metrics such as cost per querying session and overall response latency.
  arXiv  Detail & Related papers  (2024-12-12T06:27:12Z)
• A Unified Approach to Routing and Cascading for LLMs [5.653106385738822]
  Large language models (LLMs) embedded in various agentic systems have increased the potential of model selection strategies to improve the cost-performance tradeoff.<n>Existing strategies involve either routing, where a single model is chosen per query, or cascading, which sequentially runs increasingly larger models until a satisfactory answer is found.<n>We derive a novel optimal strategy for cascading and prove the optimality of an existing routing strategy.<n>We propose cascade routing, a unified framework that integrates routing and cascading into a theoretically optimal strategy.
  arXiv  Detail & Related papers  (2024-10-14T10:00:49Z)
• Optimising Calls to Large Language Models with Uncertainty-Based Two-Tier Selection [80.63946798650653]
  Decision centers on whether to use a large LLM with better performance or a smaller one with reduced costs. We propose a simpler solution; we use only the uncertainty of the generations of the small LLM as the decision criterion. Our experiments reveal this simple solution optimally balances cost and performance, outperforming existing methods on 25 out of 27 experimental setups.
  arXiv  Detail & Related papers  (2024-05-03T14:38:59Z)
• Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing [53.748685766139715]
  Large language models (LLMs) excel in most NLP tasks but also require expensive cloud servers for deployment due to their size. We propose a hybrid inference approach which combines their respective strengths to save cost and maintain quality. In experiments our approach allows us to make up to 40% fewer calls to the large model, with no drop in response quality.
  arXiv  Detail & Related papers  (2024-04-22T23:06:42Z)
• On Optimal Caching and Model Multiplexing for Large Model Inference [66.50550915522551]
  Large Language Models (LLMs) and other large foundation models have achieved noteworthy success, but their size exacerbates existing resource consumption and latency challenges. We study two approaches for mitigating these challenges: employing a cache to store previous queries and learning a model multiplexer to choose from an ensemble of models for query processing.
  arXiv  Detail & Related papers  (2023-06-03T05:01:51Z)
• Exploring Sparse Expert Models and Beyond [51.90860155810848]
  Mixture-of-Experts (MoE) models can achieve promising results with outrageous large amount of parameters but constant computation cost. We propose a simple method called expert prototyping that splits experts into different prototypes and applies $k$ top-$1$ routing. This strategy improves the model quality but maintains constant computational costs, and our further exploration on extremely large-scale models reflects that it is more effective in training larger models.
  arXiv  Detail & Related papers  (2021-05-31T16:12:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.