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Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models

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arxiv 2311.08692 v1 pith:RBYLWJVR submitted 2023-11-15 cs.CL cs.LG

Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models

Keming Lu , Hongyi Yuan , Runji Lin , Junyang Lin , Zheng Yuan , Chang Zhou , Jingren Zhou This is my paper
classification cs.CL cs.LG
keywords llmsmodelrewardroutingzootercomputationensembleexpertise
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The complementary potential of Large Language Models (LLM) assumes off-the-shelf LLMs have heterogeneous expertise in a wide range of domains and tasks so that an ensemble of LLMs can achieve consistently better performance. Existing ensemble methods for LLMs mainly focus on reward model ranking of outputs, leading to significant computation overhead. To combat this issue, we revisit the complementary potential of LLMs and further elaborate it by mining latent expertise with off-the-shelf reward models. We propose Zooter, a reward-guided routing method distilling rewards on training queries to train a routing function, which can precisely distribute each query to the LLM with expertise about it. We also integrate a tag-based label enhancement to mitigate noise from uncertainty when using rewards as silver supervision. Zooter shows computation efficiency in inference as it introduces only a minor computation overhead of a routing function compared with reward model ranking methods. We evaluate Zooter on a comprehensive benchmark collection with 26 subsets on different domains and tasks. Zooter outperforms the best single model on average and ranks first on 44% of tasks, even surpassing multiple reward model ranking methods.

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Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Unsolvability Ceiling in Multi-LLM Routing: An Empirical Study of Evaluation Artifacts

    cs.LG 2026-05 unverdicted novelty 7.0

    Evaluation artifacts substantially inflate the measured unsolvability ceiling in multi-LLM routing, leading to distorted router training and overstated headroom.

  2. Rethinking Predictive Modeling for LLM Routing: When Simple kNN Beats Complex Learned Routers

    cs.LG 2025-05 conditional novelty 7.0

    A well-tuned kNN router matches or exceeds state-of-the-art learned routers on new standardized benchmarks spanning instruction, QA, reasoning, and the first multi-modal visual routing dataset, due to locality of mode...

  3. Decentralized Aggregation of LLM Predictions via Wagering Mechanisms

    cs.AI 2026-07 accept novelty 6.5

    A leave-one-out wagering payout makes LLM aggregation weights equal expected score advantage, yielding DSIC predictions, decentralized wager learning, and performance matching centralized routers.

  4. The Routing Plateau: Understanding and Breaking the Accuracy Limits of LLM Routers

    cs.LG 2026-05 unverdicted novelty 6.0

    LLM routers across 21 methods on 5 benchmarks converge to similar accuracy below oracle due to learning global performance trends rather than fine-grained query signals.

  5. HyDRA: Hybrid Dynamic Routing Architecture for Heterogeneous LLM Pools

    cs.CL 2026-05 unverdicted novelty 6.0

    HyDRA routes queries to cost-effective LLMs by predicting multi-dimensional capability requirements with a multi-head encoder and applying shortfall matching against configuration-defined model profiles, delivering up...

  6. Optimal-Agent-Selection: State-Aware Routing Framework for Efficient Multi-Agent Collaboration

    cs.AI 2025-11 conditional novelty 6.0

    A state-aware contrastive router that selects the most relevant agent at each step improves multi-agent LLM accuracy by up to 23.8% while using a fraction of the tokens of fixed-pipeline baselines.

  7. RouteLLM: Learning to Route LLMs with Preference Data

    cs.LG 2024-06 unverdicted novelty 6.0

    Router models trained on preference data dynamically select between strong and weak LLMs, cutting inference costs by more than 2x on benchmarks with no quality loss and showing transfer to new model pairs.

  8. Towards Generalized Routing: Model and Agent Orchestration for Adaptive and Efficient Inference

    cs.MA 2025-09 reject novelty 5.0

    MoMA routes each query to a specialized agent or to the cost-optimal LLM, using judge-trained performance scores, a Pareto cost frontier, and TOPSIS selection.

  9. A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement

    cs.CL 2025-07 unverdicted novelty 5.0

    SMCS coordinates 15 open-source LLMs via retrieval-based prior selection and exploration-exploitation posterior enhancement, outperforming GPT-4.1 by 5.36% and GPT-o3-mini by 5.28% on eight benchmarks.