  The impressive capabilities in Large Language Models (LLMs) provide a
powerful approach to reimagine users' typing experience. This paper
demonstrates Proofread, a novel Gboard feature powered by a server-side LLM in
Gboard, enabling seamless sentence-level and paragraph-level corrections with a
single tap. We describe the complete system in this paper, from data
generation, metrics design to model tuning and deployment. To obtain models
with sufficient quality, we implement a careful data synthetic pipeline
tailored to online use cases, design multifaceted metrics, employ a two-stage
tuning approach to acquire the dedicated LLM for the feature: the Supervised
Fine Tuning (SFT) for foundational quality, followed by the Reinforcement
Learning (RL) tuning approach for targeted refinement. Specifically, we find
sequential tuning on Rewrite and proofread tasks yields the best quality in SFT
stage, and propose global and direct rewards in the RL tuning stage to seek
further improvement. Extensive experiments on a human-labeled golden set showed
our tuned PaLM2-XS model achieved 85.56\% good ratio. We launched the feature
to Pixel 8 devices by serving the model on TPU v5 in Google Cloud, with
thousands of daily active users. Serving latency was significantly reduced by
quantization, bucket inference, text segmentation, and speculative decoding.
Our demo could be seen in \href{https://youtu.be/4ZdcuiwFU7I}{Youtube}.
