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[2602.01428] Improving the Trade-off Between Watermark Strength and Speculative Sampling Efficiency for Language Models
Summary
This paper explores the balance between watermark strength and speculative sampling efficiency in language models, proposing a new approach to optimize both aspects simultaneously.
Why It Matters
The findings address a critical challenge in deploying watermarking techniques for language models, which is essential for tracing output provenance while maintaining efficiency. This research could enhance the practical application of watermarking in AI systems, contributing to better security and reliability in generative AI outputs.
Key Takeaways
- Introduces a quantitative measure of watermark strength for language models.
- Characterizes the trade-off between watermark strength and sampling efficiency as a constrained optimization problem.
- Derives explicit Pareto curves for existing watermarking schemes.
- Proposes a mechanism to inject pseudorandomness to enhance watermark strength without sacrificing efficiency.
- Demonstrates improved detectability in experiments, paving the way for practical deployment.
Computer Science > Machine Learning arXiv:2602.01428 (cs) [Submitted on 1 Feb 2026 (v1), last revised 23 Feb 2026 (this version, v2)] Title:Improving the Trade-off Between Watermark Strength and Speculative Sampling Efficiency for Language Models Authors:Weiqing He, Xiang Li, Li Shen, Weijie Su, Qi Long View a PDF of the paper titled Improving the Trade-off Between Watermark Strength and Speculative Sampling Efficiency for Language Models, by Weiqing He and 4 other authors View PDF HTML (experimental) Abstract:Watermarking is a principled approach for tracing the provenance of large language model (LLM) outputs, but its deployment in practice is hindered by inference inefficiency. Speculative sampling accelerates inference, with efficiency improving as the acceptance rate between draft and target models increases. Yet recent work reveals a fundamental trade-off: higher watermark strength reduces acceptance, preventing their simultaneous achievement. We revisit this trade-off and show it is not absolute. We introduce a quantitative measure of watermark strength that governs statistical detectability and is maximized when tokens are deterministic functions of pseudorandom numbers. Using this measure, we fully characterize the trade-off as a constrained optimization problem and derive explicit Pareto curves for two existing watermarking schemes. Finally, we introduce a principled mechanism that injects pseudorandomness into draft-token acceptance, ensuring maximal watermark s...
