@inproceedings{ji-etal-2025-raven-pinpointing,
title = "{RAVEN}++: Pinpointing Fine-Grained Violations in Advertisement Videos with Active Reinforcement Reasoning",
author = "Ji, Deyi and
Yang, Yuekui and
Liu, Liqun and
Shu, Peng and
Wu, Haiyang and
Tang, Shaogang and
Chen, Xudong and
Ma, Shaoping and
Chen, Tianrun and
Zhu, Lanyun",
editor = "Potdar, Saloni and
Rojas-Barahona, Lina and
Montella, Sebastien",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2025",
address = "Suzhou (China)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-industry.1/",
doi = "10.18653/v1/2025.emnlp-industry.1",
pages = "1--10",
ISBN = "979-8-89176-333-3",
abstract = "Advertising (Ad) is a cornerstone of the digital economy, yet the moderation of video advertisements remains a significant challenge due to their complexity and the need for precise violation localization. While recent advancements, such as the RAVEN model, have improved coarse-grained violation detection, critical gaps persist in fine-grained understanding, explainability, and generalization. To address these limitations, we propose RAVEN++, a novel framework that introduces three key innovations: 1) Active Reinforcement Learning (RL), which dynamically adapts training to samples of varying difficulty; 2) Fine-Grained Violation Understanding, achieved through hierarchical reward functions and reasoning distillation; and 3) Progressive Multi-Stage Training, which systematically combines knowledge injection, curriculum-based passive RL, and active RL. Extensive experiments on both public and proprietary datasets, on both offline scenarios and online deployed A/B Testing, demonstrate that RAVEN++ outperforms general-purpose LLMs and specialized models like RAVEN in terms of fine-grained violation understanding, reasoning capabilities, and generalization ability."
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%0 Conference Proceedings
%T RAVEN++: Pinpointing Fine-Grained Violations in Advertisement Videos with Active Reinforcement Reasoning
%A Ji, Deyi
%A Yang, Yuekui
%A Liu, Liqun
%A Shu, Peng
%A Wu, Haiyang
%A Tang, Shaogang
%A Chen, Xudong
%A Ma, Shaoping
%A Chen, Tianrun
%A Zhu, Lanyun
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F ji-etal-2025-raven-pinpointing
%X Advertising (Ad) is a cornerstone of the digital economy, yet the moderation of video advertisements remains a significant challenge due to their complexity and the need for precise violation localization. While recent advancements, such as the RAVEN model, have improved coarse-grained violation detection, critical gaps persist in fine-grained understanding, explainability, and generalization. To address these limitations, we propose RAVEN++, a novel framework that introduces three key innovations: 1) Active Reinforcement Learning (RL), which dynamically adapts training to samples of varying difficulty; 2) Fine-Grained Violation Understanding, achieved through hierarchical reward functions and reasoning distillation; and 3) Progressive Multi-Stage Training, which systematically combines knowledge injection, curriculum-based passive RL, and active RL. Extensive experiments on both public and proprietary datasets, on both offline scenarios and online deployed A/B Testing, demonstrate that RAVEN++ outperforms general-purpose LLMs and specialized models like RAVEN in terms of fine-grained violation understanding, reasoning capabilities, and generalization ability.
%R 10.18653/v1/2025.emnlp-industry.1
%U https://aclanthology.org/2025.emnlp-industry.1/
%U https://doi.org/10.18653/v1/2025.emnlp-industry.1
%P 1-10
Markdown (Informal)
[RAVEN++: Pinpointing Fine-Grained Violations in Advertisement Videos with Active Reinforcement Reasoning](https://aclanthology.org/2025.emnlp-industry.1/) (Ji et al., EMNLP 2025)
ACL
- Deyi Ji, Yuekui Yang, Liqun Liu, Peng Shu, Haiyang Wu, Shaogang Tang, Xudong Chen, Shaoping Ma, Tianrun Chen, and Lanyun Zhu. 2025. RAVEN++: Pinpointing Fine-Grained Violations in Advertisement Videos with Active Reinforcement Reasoning. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1–10, Suzhou (China). Association for Computational Linguistics.