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cloud.google.com·8h ago
AI Research
arXiv CS.AI·Aug 10·5 min read
Policy-Masked Private Experts: Auditable and Reversible Capability Access Control in Sparse MoE Models
TL;DR
Policy-Masked Private Experts introduces a method for controlling access to specific parameters in sparse Mixture-of-Experts (MoE) models, ensuring unauthorized requests cannot access private experts. This approach demonstrates a significant improvement in tool use accuracy while maintaining public model capabilities.
✦ Why It Matters
Implement access control mechanisms in your models to enhance security and maintain performance.
Key Takeaways
- Utilize Policy-Masked Private Experts for secure parameter access control.
- Test models like Qwen3-30B-A3B for unauthorized access scenarios.
- Leverage the disjoint expert branch for improved task utility.
- Ensure compliance with access policies to maintain model integrity.
How It Works
The Policy-Masked Private Experts method involves freezing a pretrained sparse Mixture-of-Experts (MoE) model and training a separate expert branch. This allows for selective access to either public or private parameters before routing requests, ensuring that unauthorized requests cannot access private experts.
The system maintains a clear separation between execution control and task utility.