Balancing Detectability and Fluency in Neural Text Generation via Reinforcement-Guided Watermark Placement

Abstract

The exponential growth of neural text generation systems has intensified concerns regarding content authenticity and model ownership protection in the artificial intelligence ecosystem. This research introduces a reinforcement learning framework that dynamically optimizes watermark placement during text generation to achieve superior balance between detection reliability and linguistic fluency. Traditional watermarking approaches apply uniform strategies across all generation contexts, resulting in either compromised text quality or insufficient watermark robustness. Our method employs a policy network trained through proximal policy optimization that learns context-aware placement decisions, identifying optimal positions and strengths for watermark injection based on local linguistic characteristics and downstream detectability requirements. The framework introduces a dual-objective reward function that simultaneously maximizes watermark detection confidence while minimizing perplexity degradation and semantic distortion. Through comprehensive evaluation on diverse text generation benchmarks including news article synthesis, dialogue generation, and creative writing tasks, we demonstrate that reinforcement-guided placement achieves 97.8% detection accuracy while maintaining perplexity within 1.9% of unwatermarked baselines, outperforming static watermarking strategies by 12.3% in quality-detectability trade-offs. The approach exhibits robust performance under adversarial conditions including paraphrasing attacks, text truncation, and insertion manipulations, with watermark retention rates exceeding 89% across attack scenarios. Our contributions establish reinforcement learning as a principled framework for adaptive watermarking in neural text generation systems, providing practical solutions for intellectual property protection in production language model deployments.