Language model watermarking schemes fall into two broad categories: inference-time methods, which modify the decoding process at generation time, and model-embedded methods, which encode a secret signal directly into the model weights. Inference-time watermarking methods incur additional inference overhead and are not applicable in the increasingly prevalent open-weight model settings. In contrast, model-embedded watermarks introduce no additional inference latency-generation uses the standard sampling pipeline-and are particularly well-suited to open-weight settings. However, existing model-embedded approaches such as GaussMark face a fundamental quality-detectability trade-off: achieving strong detection power typically requires weight perturbations that noticeably degrade generation quality. Our key insight is that regularizing toward a high-quality reference distribution decouples quality preservation from proximity to the base model, enabling a better quality-detectability trade-off. Building on this insight, we introduce MarkTune, a theoretically grounded on-policy fine-tuning framework that uses the GaussMark detection statistic as a reward while preserving generation quality through cross-entropy on high-quality human text or KL divergence to a stronger oracle model. Empirically, MarkTune substantially improves the quality-detectability frontier of GaussMark, approaching the detectability of strong inference-time schemes while preserving generation quality and downstream task performance. Marktune also remains robust under the evaluated text-editing and open-weight attacks and generalizes across datasets, retaining substantial detection power on corpora unseen during training. Code is available at https://github.com/zhyzmath/MarkTune-LLM-Watermarking.
State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences（数学科学国家重点实验室，数学与系统科学研究院，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of AI for Industries, Nanjing, China（产业人工智能研究院，南京，中国）
Server-Enforced Watermarking in U-Shaped Split Federated Learning
Software Engineering Institute, East China Normal University（东华大学软件工程学院）；College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）；College of Control Science and Engineering, China University of Petroleum (East China)（中国石油大学（华东）控制科学与工程学院）
MirrorMark: Generalizable Mirrored Sampling for Multi-bit LLM Watermarking
George Mason University（乔治·马歇尔大学）；Department of Computer Science（计算机科学系）；Wireless Cyber Center（无线网络安全中心）；Department of Electrical and Computer Engineering（电气与计算机工程系）
Invisible Ink, Visible Lies: How Production Watermarking Causes LLMs to Hallucinate
Nanjing University of Science and Technology（南京理工大学）；Anhui University of Technology（安徽工业大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Luxembourg（卢森堡大学）；CSIRO（澳大利亚联邦科学与工业研究组织）
TabularMark: Watermarking Tabular Datasets for Machine Learning
Zhejiang University（浙江大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；McMaster University（麦克马斯特大学）；Tokyo Institute of Technology（东京工业大学）；Emory University（埃默里大学）
RMCW: A Deletion-Robust Watermark Based on Reed--Muller Codes for Language Models
Tsinghua University（清华大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；Shanghai Qi Zhi Institute（上海期智研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Chinese Academy of Sciences（中国科学院）；Xiongan AI Institute（雄安人工智能研究院）
