@inproceedings{hu-etal-2026-raca,
title = "{RACA}-{PODS}: Reasoning-Informed First-Token Prediction for Query-Document Relevance in {W}e{C}hat Search",
author = "Hu, Yupeng and
Liao, Dongliang and
Ding, Changxing and
Qiu, Mengchuan and
Liang, Huasheng and
Yan, Qiang and
Wang, Junhong",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.209/",
pages = "3520--3534",
ISBN = "979-8-89176-460-6",
abstract = "Applying LLMs to query-document relevance estimation in industrial document retrieval faces two key challenges: the complexity and heterogeneity of Web content, and stringent latency and resource constraints. To address them, we propose an automated framework with two components: **R**eflection-**A**ware **C**ollaborative **A**gent (**RACA**) and **P**rogressive **O**ptimization **D**istillation **S**trategy (**PODS**). RACA uses collaborative reasoning and reflection to produce relevance labels and structured rationales. PODS stratifies training data by difficulty and combines supervised fine-tuning with Reasoning-Informed GRPO. Rationale supervision improves the student{'}s first-token relevance decision, while deployment stops after that token and avoids explicit rationale generation. Together, these mechanisms enable reasoning-informed first-token prediction that preserves the benefits of structured rationale supervision under stringent latency and resource constraints. Extensive offline experiments and a 10-day online A/B test in WeChat Search demonstrate the resulting accuracy{--}efficiency trade-off."
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<abstract>Applying LLMs to query-document relevance estimation in industrial document retrieval faces two key challenges: the complexity and heterogeneity of Web content, and stringent latency and resource constraints. To address them, we propose an automated framework with two components: **R**eflection-**A**ware **C**ollaborative **A**gent (**RACA**) and **P**rogressive **O**ptimization **D**istillation **S**trategy (**PODS**). RACA uses collaborative reasoning and reflection to produce relevance labels and structured rationales. PODS stratifies training data by difficulty and combines supervised fine-tuning with Reasoning-Informed GRPO. Rationale supervision improves the student’s first-token relevance decision, while deployment stops after that token and avoids explicit rationale generation. Together, these mechanisms enable reasoning-informed first-token prediction that preserves the benefits of structured rationale supervision under stringent latency and resource constraints. Extensive offline experiments and a 10-day online A/B test in WeChat Search demonstrate the resulting accuracy–efficiency trade-off.</abstract>
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%0 Conference Proceedings
%T RACA-PODS: Reasoning-Informed First-Token Prediction for Query-Document Relevance in WeChat Search
%A Hu, Yupeng
%A Liao, Dongliang
%A Ding, Changxing
%A Qiu, Mengchuan
%A Liang, Huasheng
%A Yan, Qiang
%A Wang, Junhong
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F hu-etal-2026-raca
%X Applying LLMs to query-document relevance estimation in industrial document retrieval faces two key challenges: the complexity and heterogeneity of Web content, and stringent latency and resource constraints. To address them, we propose an automated framework with two components: **R**eflection-**A**ware **C**ollaborative **A**gent (**RACA**) and **P**rogressive **O**ptimization **D**istillation **S**trategy (**PODS**). RACA uses collaborative reasoning and reflection to produce relevance labels and structured rationales. PODS stratifies training data by difficulty and combines supervised fine-tuning with Reasoning-Informed GRPO. Rationale supervision improves the student’s first-token relevance decision, while deployment stops after that token and avoids explicit rationale generation. Together, these mechanisms enable reasoning-informed first-token prediction that preserves the benefits of structured rationale supervision under stringent latency and resource constraints. Extensive offline experiments and a 10-day online A/B test in WeChat Search demonstrate the resulting accuracy–efficiency trade-off.
%U https://aclanthology.org/2026.emnlp-industry.209/
%P 3520-3534
Markdown (Informal)
[RACA-PODS: Reasoning-Informed First-Token Prediction for Query-Document Relevance in WeChat Search](https://aclanthology.org/2026.emnlp-industry.209/) (Hu et al., EMNLP 2026)
ACL