R^3: Advertisement Compliance Rectification via Group-Relative Experience Extractor and Curriculum Reinforcement

• URL: http://arxiv.org/abs/2607.07318v1
• Date: Wed, 08 Jul 2026 12:05:41 GMT
• Title: R^3: Advertisement Compliance Rectification via Group-Relative Experience Extractor and Curriculum Reinforcement
• Abstract summary: Rigorous content moderation is crucial for online advertising but leads to millions of daily rejections.<n>We propose R3, a novel framework designed to harmonize compliance with original semantic intent preservation.<n>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.
• Score: 14.426627534391086
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Automatic Red Teaming for Implicit Vulnerabilities of Text-to-Image Models [58.59008920408855]
  We propose Adversarial Probing for Implicit VulnErabilities (AdvPIE) to expose implicit vulnerabilities without requiring access to the parameters of target models.<n> AdvPIE adopts a policy agent to generate and refine implicit adversarial prompts based on the feedback from a judge agent.<n>Experiments on standard and safety-aligned T2I models show that AdvPIE1 effectively uncovers implicit vulnerabilities.
  arXiv  Detail & Related papers  (2026-09-05T13:45:09Z)
• Deep Interest Mining with Cross-Modal Alignment for SemanticID Generation in Generative Recommendation [0.0]
  Generative Recommendation (GR) relies on Semantic IDs (SIDs) to compress trillion-scale data into learnable vocabulary sequences.<n>Existing methods suffer from three critical limitations.<n>We propose a novel framework integrating three key innovations: Deep Contextual Interest Mining (DCIM), Cross-Modal Semantic Alignment (CMSA), and Quality-Aware Reinforcement Mechanism (QARM)
  arXiv  Detail & Related papers  (2026-03-03T13:36:22Z)
• Mine and Refine: Optimizing Graded Relevance in E-commerce Search Retrieval [3.1241290518951197]
  Large scale e-commerce search demands embeddings that generalize to long tail, noisy queries.<n>We propose a two-stage "Mine and Refine" contrastive training framework for semantic text embeddings.
  arXiv  Detail & Related papers  (2026-02-19T18:56:36Z)
• RELATE: A Reinforcement Learning-Enhanced LLM Framework for Advertising Text Generation [17.34586562700226]
  In online advertising, advertising text plays a critical role in attracting user engagement and driving advertiser value.<n>We propose RELATE, a reinforcement learning-based end-to-end framework that unifies generation and objective alignment within a single model.<n>To better capture ultimate advertiser value beyond click-level signals, We incorporate conversion-oriented metrics into the objective and jointly model them with compliance constraints as multi-dimensional rewards.
  arXiv  Detail & Related papers  (2026-02-12T10:00:55Z)
• RANKVIDEO: Reasoning Reranking for Text-to-Video Retrieval [99.33724613432922]
  We introduce RANKVIDEO, a reasoning-based reranker for video retrieval.<n>RANKVIDEO explicitly reasons over query-video pairs using video content to assess relevance.<n> Experiments on the large-scale MultiVENT 2.0 benchmark demonstrate that RANKVIDEO consistently improves retrieval performance within a two-stage framework.
  arXiv  Detail & Related papers  (2026-02-02T18:40:37Z)
• RAVEN++: Pinpointing Fine-Grained Violations in Advertisement Videos with Active Reinforcement Reasoning [28.372254433147564]
  RAVEN++ is a novel framework that introduces three key innovations.<n>Active Reinforcement Learning (RL), which dynamically adapts training to samples of varying difficulty.<n>Fine-Grained Violation Understanding, achieved through hierarchical reward functions and reasoning distillation.<n>Progressive Multi-Stage Training, which systematically combines knowledge injection, curriculum-based passive RL, and active RL.
  arXiv  Detail & Related papers  (2025-11-24T14:32:13Z)
• All for law and law for all: Adaptive RAG Pipeline for Legal Research [0.8819595592190884]
  Retrieval-Augmented Generation (RAG) has transformed how we approach text generation tasks.<n>This work introduces a novel end-to-end RAG pipeline that improves upon previous baselines.
  arXiv  Detail & Related papers  (2025-08-18T17:14:03Z)
• NAP-Tuning: Neural Augmented Prompt Tuning for Adversarially Robust Vision-Language Models [72.58372335140241]
  Adversarial Prompt Tuning (AdvPT) introduced learnable text prompts to enhance adversarial robustness in Vision-Language Models (VLMs)<n>We present the Neural Augmentor framework for Multi-modal Adversarial Prompt Tuning (NAP-Tuning)<n>Our approach shows significant improvements over the strongest baselines under the challenging AutoAttack benchmark, outperforming them by 33.5% on ViT-B16 and 33.0% on ViT-B32 architectures.
  arXiv  Detail & Related papers  (2025-06-15T03:34:23Z)
• Fact-R1: Towards Explainable Video Misinformation Detection with Deep Reasoning [54.56271651170667]
  Existing methods often overfit to rigid templates and lack deep reasoning over deceptive content.<n>We introduce FakeVV, a large-scale benchmark comprising over 100,000 video-text pairs with fine-grained, interpretable annotations.<n>We also propose Fact-R1, a framework that integrates deep reasoning with collaborative rule-based reinforcement learning.
  arXiv  Detail & Related papers  (2025-05-22T16:05:06Z)
• GMMFormer v2: An Uncertainty-aware Framework for Partially Relevant Video Retrieval [60.70901959953688]
  We present GMMFormer v2, an uncertainty-aware framework for PRVR. For clip modeling, we improve a strong baseline GMMFormer with a novel temporal consolidation module. We propose a novel optimal matching loss for fine-grained text-clip alignment.
  arXiv  Detail & Related papers  (2024-05-22T16:55:31Z)
• Noisy-Correspondence Learning for Text-to-Image Person Re-identification [50.07634676709067]
  We propose a novel Robust Dual Embedding method (RDE) to learn robust visual-semantic associations even with noisy correspondences. Our method achieves state-of-the-art results both with and without synthetic noisy correspondences on three datasets.
  arXiv  Detail & Related papers  (2023-08-19T05:34:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.