R³：基于组相对经验与课程强化学习的广告合规整改
SOURCE / arXiv cs.CL · R^3: Advertisement Compliance Rectification via Group-Relative Experience Extractor and Curriculum Reinforcement
原文
R^3: Advertisement Compliance Rectification via Group-Relative Experience Extractor and Curriculum Reinforcement
完整原文
arXiv:2607.07318v1 Announce Type: new Abstract: Rigorous content moderation is crucial for online advertising but leads to millions of daily rejections. This scale renders manual rectification infeasible, particularly for video advertisements. However, existing safety-driven methods often suffer from aggressive over-editing, which compromises the advertiser's original semantic intent merely to satisfy compliance. In this work, we target the rectification of textual violations in video ads, covering both speech transcripts and on-screen text. We propose R^3, a novel framework designed to harmonize compliance with original semantic intent preservation. Our approach integrates three key innovations: (1) an experience-driven data synthesis framework that bootstraps high-quality supervision via a group-Relative compliance experience extractor; (2) a curriculum Reinforcement learning strategy with hierarchical rewards designed to enforce compliance while maximizing semantic consistency; and (3) a comprehensive video Rectification framework seamlessly integrating text recognition, rewriting, and re-rendering for industrial deployment. Extensive experiments on industrial datasets and online A/B testing demonstrate that R^3 significantly outperforms state-of-the-art baselines, achieving an optimal trade-off between violation rectification and intent preservation.
归纳
本文提出R³框架，旨在协调视频广告中文本违规整改与原始语义保持。该框架包含三项关键创新：一是通过组相对合规经验提取器引导高质量监督数据合成；二是采用具有分层奖励的课程强化学习策略，在增强合规性的同时最大化语义一致性；三是整合文本识别、改写与重渲染的完整视频整改方案，便于工业部署。在工业数据集和在线A/B测试中，R³均显著优于现有方法，在违规整改与意图保持之间实现了最优权衡。
点评
自动化广告合规整改技术可能引发责任归属模糊问题，即整改后广告仍涉违规时算法设计者、广告主与平台间的法律责任如何划分。
法律视角点评
AI 生成 · 人工审核核心关切
自动化广告合规整改技术可能引发责任归属模糊问题，即整改后广告仍涉违规时算法设计者、广告主与平台间的法律责任如何划分。
实务启示
中国法律人应明确，使用AI辅助合规整改不能免除广告主及平台的实质审查义务，且需确保算法决策过程的可追溯与透明度。