---
title: "Routing to the Expert — paper by Lu et al. (2023)"
description: "Lu et al. (Alibaba) propose ZOOTER, which picks one model per query before any generation. 37 references, cited by 62 papers here."
authors: ["Keming Lu", "Hongyi Yuan", "Runji Lin", "Junyang Lin", "Zheng Yuan", "Chang Zhou", "Jingren Zhou"]
year: 2023
venue: "arXiv:2311.08692"
source: "https://arxiv.org/abs/2311.08692"
doi: "10.48550/arxiv.2311.08692"
arxiv: "2311.08692"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/lu-2023-routing-expert-efficient-reward-guided/"
---
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# Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models

**Authors:** [Keming Lu](https://md.research.elamur.ai/free/en/authors/keming-lu/), [Hongyi Yuan](https://md.research.elamur.ai/free/en/authors/hongyi-yuan/), [Runji Lin](https://md.research.elamur.ai/free/en/authors/runji-lin/), [Junyang Lin](https://md.research.elamur.ai/free/en/authors/junyang-lin/), [Zheng Yuan](https://md.research.elamur.ai/free/en/authors/zheng-yuan/), [Chang Zhou](https://md.research.elamur.ai/free/en/authors/chang-zhou/) and [Jingren Zhou](https://md.research.elamur.ai/free/en/authors/jingren-zhou/) · **Year:** 2023 · **Venue:** arXiv:2311.08692 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2311.08692) · [PDF](https://arxiv.org/pdf/2311.08692) · [DOI 10.48550/arxiv.2311.08692](https://doi.org/10.48550/arxiv.2311.08692) · [arXiv 2311.08692](https://arxiv.org/abs/2311.08692) · [OpenAlex](https://openalex.org/W4388747974)

**Citations:** 4 citations (OpenAlex, 2026-10-14) · [62](https://md.research.elamur.ai/free/en/papers/lu-2023-routing-expert-efficient-reward-guided/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Open-source chat LLMs differ in strengths across domains, so combining them could beat any single model. The usual combination method, reward model ranking (RMR), runs every candidate on every query and lets a reward model pick the best output, which multiplies inference cost. Lu et al. (Alibaba) propose ZOOTER, which picks one model per query before any generation. During training, each candidate LLM answers a varied pool of queries, and QwenRM scores the answers. These scores become soft targets for a small router built on mdeberta-v3-base, trained by knowledge distillation. Reward-model scores are noisy, so the authors blend each query's rewards with averages over queries sharing the same tag. The router is tested on six candidate models across 26 subsets grouped into AlpacaEval, FLASK, MT-Bench and standard benchmarks. ZOOTER beats the best single model on average while adding only about 86M router parameters. It also outperforms several RMR setups. This makes model ensembles practical when compute is limited.

## Key points
- The six routing candidates are LLaMA-2-Chat, WizardLM, WizardCoder, WizardMath, Vicuna and OpenChat. LLaMA-2-Chat is the best model on average (BMA).
- ZOOTER is the top system on 44% of subtasks, compared with 31% for BMA. Its largest gain is +0.39 on MT-Bench.
- ZOOTER beats RMR using OAssistRM, LLM-Blender and Auto-J at a fraction of their cost. It still trails RMR with QwenRM overall.
- RMR with UltraRM is the strongest ensemble, with a mean task rank of 1.53 and first place on 72% of subtasks. RMR with an oracle ranker even beats GPT-4 on AlpacaEval and FLASK. However, reward models judge MMLU, GSM8K and HumanEval answers poorly.
- The tag-based label enhancement works best at β = 0.3 (mean task rank 1.97). Using only tag-level rewards (β = 0) beats using only per-sample rewards (β = 1), which supports the claim that per-sample reward noise hurts training.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Alibaba Group](https://md.research.elamur.ai/free/en/companies/alibaba-group/) — Company, Hangzhou, China · 201 works · 17,173 citations

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🧭 Topics (1): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 3,894 works

## Cite

Cite as: Keming Lu, Hongyi Yuan, Runji Lin, Junyang Lin, Zheng Yuan, Chang Zhou and Jingren Zhou. 2023. Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models. arXiv:2311.08692.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{lu2023routing,
  title = {{Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models}},
  author = {Keming Lu, Hongyi Yuan, Runji Lin, Junyang Lin, Zheng Yuan, Chang Zhou and Jingren Zhou},
  year = {2023},
  doi = {10.48550/arxiv.2311.08692},
  eprint = {2311.08692},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2311.08692}
}
```

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## References

37 references, 24 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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37. Dataset Amount ultrachat sharedgpt wizardlm(sharedgpt) wizardlm(alpaca) alpaca repair openchat flan math unnatural dmcc dolly oasst lima mbpp 18,588 10432 5325 5145 2186 1034 1033 862 849 582 573 560 183 70 43 Table 3: Composition of D IV I NSTRUCT [find](https://scholar.google.com/scholar?q=Dataset+Amount+ultrachat+sharedgpt+wizardlm%28sharedgpt%29+wizardlm%28alpaca%29+alpaca+repair+openchat+flan+math+unnatural+dmcc+dolly+oasst+lima+mbpp+18%2C588+10432+5325+5145+2186+1034+1033+862+849+582+573+560+)

*Updated 2 November 2026*