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English(EN) Optimizing Token Choice for Code Watermarking: An RL Approach

新的强化学习方法增强了LLM代码水印

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了CodeTracer，一个用于大型语言模型生成代码的水印框架。该系统采用强化学习方法，在代码生成过程中智能地偏向Token选择，确保水印可检测且不影响代码功能。CodeTracer将执行反馈与水印信号相结合，以优化策略学习，并在保持代码完整性和水印可检测性方面，其性能优于现有方法。 AI

影响 引入了一种保护LLM生成代码知识产权的新颖方法，可能影响代码安全和所有权。

排序理由 学术论文，详细介绍了一种新颖的代码水印方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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新的强化学习方法增强了LLM代码水印

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Tool
学术论文，详细介绍了一种新颖的代码水印方法。[lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
paper, safety
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151 days old
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Zhimeng Guo, Huaisheng Zhu, Siyuan Xu, Hangfan Zhang, Teng Xiao, Minhao Cheng ·

    优化代码水印的Token选择：一种强化学习方法

    arXiv:2508.11925v3 Announce Type: replace-cross Abstract: Protecting intellectual property on LLM-generated code necessitates effective watermarking systems that can operate within code's highly structured, syntactically constrained nature. In this work, we introduce CodeTracer, …

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