PathGuide: Dynamic Classifier-Free Guidance via On-Policy Transport Alignment
While modern generative models excel at modeling complex data, precise inference-time control in conditional generation remains a critical challenge. Classifier-free guidance (CFG) is a primary mechanism for such control, yet it is typically treated as a static tuning parameter. In flow-based models, however, the guidance scale fundamentally dictates the velocity field and the resulting probability path, making guidance selection a dynamic path-optimization problem. We introduce PathGuide, a framework that reformulates scalar CFG selection as an on-policy transport problem. Leveraging the weak form of the continuity equation, we derive a selection criterion with a direct path-correctness ...
| Authors | Avishag Nevo, Tamir Hazan |
|---|---|
| Primary category | cs.LG |
| Published | 29 August 2026 |
| Last updated at source | 5 September 2026 |
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