@inproceedings{liu-etal-2026-scaling-reinforcement,
title = "Scaling Reinforcement Learning for Content Moderation with Large Language Models",
author = "Liu, Rui and
Lu, Yuchen and
Hou, Zhenyu and
Xiong, Fangzhou and
Zhang, Xiaoyang and
Jian, Changshu and
Zhu, Zhicheng and
Ma, Jiayuan and
Tao, Jacob and
Unlu Menevse, Merve and
Li, Han and
Gupta, Chaitali and
Peng, Xiaochang and
Lin, Suwen and
Bai, Chongyang and
Mei, Shike and
Cui, Hang and
Qin, Yang and
Tang, Shuo and
Gaedtke, Jason and
Mittal, Arpit and
Firooz, Hamed",
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.17/",
pages = "259--274",
ISBN = "979-8-89176-460-6",
abstract = "Large-scale content moderation requires models that can apply complex, evolving policies under limited expert supervision. We present an empirical study of reinforcement learning (RL) for policy-grounded moderation across three production-derived tasks. Using established RL and inference primitives, we develop three integrity-specific designs: a policy-grounded full-trace reward, rationale-marginalized Monte Carlo scoring, and policy-structured reflection. Each design addresses an observed failure mode, including reasoning-length collapse and polarized confidence estimates. Across the three tasks, RL-Only systems trained on 200{--}836 labeled examples remain competitive with supervised fine-tuning (SFT) systems trained on thousands of examples, corresponding to up to approximately one order of magnitude greater label efficiency at the evaluated operating points. Performance generally improves with additional data and training compute before saturating, although larger rollout counts exhibit variability. The strongest SFT{\textrightarrow}RL systems attain point-estimate performance comparable to policy-trained full-time employee (FTE) reviewers across the three tasks."
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<abstract>Large-scale content moderation requires models that can apply complex, evolving policies under limited expert supervision. We present an empirical study of reinforcement learning (RL) for policy-grounded moderation across three production-derived tasks. Using established RL and inference primitives, we develop three integrity-specific designs: a policy-grounded full-trace reward, rationale-marginalized Monte Carlo scoring, and policy-structured reflection. Each design addresses an observed failure mode, including reasoning-length collapse and polarized confidence estimates. Across the three tasks, RL-Only systems trained on 200–836 labeled examples remain competitive with supervised fine-tuning (SFT) systems trained on thousands of examples, corresponding to up to approximately one order of magnitude greater label efficiency at the evaluated operating points. Performance generally improves with additional data and training compute before saturating, although larger rollout counts exhibit variability. The strongest SFT→RL systems attain point-estimate performance comparable to policy-trained full-time employee (FTE) reviewers across the three tasks.</abstract>
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%0 Conference Proceedings
%T Scaling Reinforcement Learning for Content Moderation with Large Language Models
%A Liu, Rui
%A Lu, Yuchen
%A Hou, Zhenyu
%A Xiong, Fangzhou
%A Zhang, Xiaoyang
%A Jian, Changshu
%A Zhu, Zhicheng
%A Ma, Jiayuan
%A Tao, Jacob
%A Unlu Menevse, Merve
%A Li, Han
%A Gupta, Chaitali
%A Peng, Xiaochang
%A Lin, Suwen
%A Bai, Chongyang
%A Mei, Shike
%A Cui, Hang
%A Qin, Yang
%A Tang, Shuo
%A Gaedtke, Jason
%A Mittal, Arpit
%A Firooz, Hamed
%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 liu-etal-2026-scaling-reinforcement
%X Large-scale content moderation requires models that can apply complex, evolving policies under limited expert supervision. We present an empirical study of reinforcement learning (RL) for policy-grounded moderation across three production-derived tasks. Using established RL and inference primitives, we develop three integrity-specific designs: a policy-grounded full-trace reward, rationale-marginalized Monte Carlo scoring, and policy-structured reflection. Each design addresses an observed failure mode, including reasoning-length collapse and polarized confidence estimates. Across the three tasks, RL-Only systems trained on 200–836 labeled examples remain competitive with supervised fine-tuning (SFT) systems trained on thousands of examples, corresponding to up to approximately one order of magnitude greater label efficiency at the evaluated operating points. Performance generally improves with additional data and training compute before saturating, although larger rollout counts exhibit variability. The strongest SFT→RL systems attain point-estimate performance comparable to policy-trained full-time employee (FTE) reviewers across the three tasks.
%U https://aclanthology.org/2026.emnlp-industry.17/
%P 259-274
Markdown (Informal)
[Scaling Reinforcement Learning for Content Moderation with Large Language Models](https://aclanthology.org/2026.emnlp-industry.17/) (Liu et al., EMNLP 2026)
ACL
- Rui Liu, Yuchen Lu, Zhenyu Hou, Fangzhou Xiong, Xiaoyang Zhang, Changshu Jian, Zhicheng Zhu, Jiayuan Ma, Jacob Tao, Merve Unlu Menevse, Han Li, Chaitali Gupta, Xiaochang Peng, Suwen Lin, Chongyang Bai, Shike Mei, Hang Cui, Yang Qin, Shuo Tang, Jason Gaedtke, Arpit Mittal, and Hamed Firooz. 2026. Scaling Reinforcement Learning for Content Moderation with Large Language Models. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 259–274, Budapest, Hungary. Association for Computational Linguistics.