RLCascadeRouter: Quality-Estimator-Free Cascade Routing via Reinforcement Learning

• URL: http://arxiv.org/abs/2608.15817v1
• Date: Sun, 16 Aug 2026 15:47:04 GMT
• Title: RLCascadeRouter: Quality-Estimator-Free Cascade Routing via Reinforcement Learning
• Abstract summary: We propose a quality-estimator-free framework that formulates cascade routing as a Markov decision process.<n>It uses trajectory returns and advantages to directly optimize the performance-cost objective.
• Score: 6.614808404204836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The growing ecosystem of large language models (LLMs) offers huge potential to optimize performance-cost trade-offs. However, their heterogeneous capabilities and inference costs make efficiently routing queries a significant challenge. Existing paradigms are inflexible: one-shot routers commit before observing responses, whereas conventional cascades stop adaptively but follow a fixed model order. Cascade routing removes both restrictions by reconsidering whether to stop or invoke another model after each response. Current methods use a predict-then-optimize pipeline estimating response quality and future model utility. However, prediction loss for quality or utility is not equivalent to routing-decision loss. A lower prediction error does not necessarily yield a better action; a small boundary-crossing error can reverse a ``stop'' or model-selection decision. Therefore, we propose RLCascadeRouter, a quality-estimator-free framework that formulates cascade routing as a Markov decision process with actions comprising ``stop'' and model selection. It uses trajectory returns and advantages to directly optimize the performance-cost objective. Its Cascade Policy Network models candidate complementarity for model selection and remaining-action value for stopping, eliminating independent post-hoc response-quality estimators. Evaluated across ten LLMRouterBench benchmarks with thirteen LLMs, RLCascadeRouter outperforms strong baselines and achieves superior performance-cost trade-offs. It incorporates unseen models without retraining, and ablation studies validate both policy components.

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)
• 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)
• ConsRoute:Consistency-Aware Adaptive Query Routing for Cloud-Edge-Device Large Language Models [7.869130026927]
  ConsRoute is a lightweight, semantic-aware, and adaptive routing framework for large language models.<n>We show that ConsRoute achieves near-cloud performance (>=95%) while reducing end-to-end latency and inference cost by nearly 40%.
  arXiv  Detail & Related papers  (2026-03-22T13:54:12Z)
• 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)
• Arbitrage: Efficient Reasoning via Advantage-Aware Speculation [71.45710345765528]
  Speculative Decoding accelerates inference by employing a fast but inaccurate draft model to autoregressively propose tokens.<n>But due to unnecessary rejections caused by token mismatches in semantically equivalent steps, traditional token-level Speculative Decoding struggles in reasoning tasks.<n>We propose Arbitrage, a novel step-level speculative generation framework that routes generation dynamically based on the relative advantage between draft and target models.
  arXiv  Detail & Related papers  (2025-12-04T17:50:53Z)
• 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)
• LLM Routing with Dueling Feedback [49.67815163970033]
  We study the problem of selecting the best model for each query while balancing user satisfaction, model expertise, and inference cost.<n>We formulate routing as contextual dueling bandits, learning from pairwise preference feedback rather than absolute scores.<n>We introduce Category-Calibrated Fine-Tuning (CCFT), a representation-learning method that derives model embeddings from offline data using contrastive fine-tuning with categorical weighting.
  arXiv  Detail & Related papers  (2025-10-01T12:52:25Z)
• Causal LLM Routing: End-to-End Regret Minimization from Observational Data [3.3580884064577616]
  LLM routing aims to select the most appropriate model for each query.<n>Prior approaches typically adopt a decoupled strategy, where the metrics are first predicted and the model is then selected based on these estimates.<n>We propose a causal end-to-end framework that learns routing policies by minimizing decision-making regret from observational data.
  arXiv  Detail & Related papers  (2025-05-21T21:34:18Z)
• Contextual Linear Optimization with Partial Feedback [35.38485630117593]
  We propose a class of offline learning algorithms for Contextual linear optimization (CLO) with different types of feedback.<n>We provide a novel fast-rate regret bound for IERM that allows for misspecified model classes and flexible choices of estimation methods.
  arXiv  Detail & Related papers  (2024-05-26T13:27:27Z)

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.