William Hang

Title: William Hang and His Patent on Secure Agent Model Training.

Introduction

William Hang is an inventor based in San Francisco, California. His patent work concerns security measures for training agent-based machine-learning models.

Latest Patents

Hang is associated with 1 patent, titled “Utilizing a Multi-Layer Security Architecture for Agent Reinforcement Fine-Tuning of a Machine Learning Model.” The patent describes systems and methods for applying security checks throughout a reinforcement fine-tuning pipeline. These checks can filter training data, detect content that should be refused, identify chain-of-thought leaks, and provide governance oversight before, during, and after training. The disclosure also describes stateful trajectory rollouts, which use unique identifiers to coordinate concurrent tasks and environments while maintaining consistency across external-tool calls and grading interactions.

Career Highlights

Hang is associated with OpenAI OpCo, LLC. His patent focuses on integrating security controls into the development and fine-tuning of agent machine-learning models.

Collaborations

The provided information names John Allard and James Blomo as Hang’s coworkers. It does not specify their roles in the patent or any direct collaboration with him.

Conclusion

William Hang’s patent addresses safeguards and coordination in agent-model training, reflecting an emphasis on secure and governed machine-learning development.