OVIG: Optimistic Verification of AI Training Integrity via Gradient Signals
The rapid growth of AI has increased the demand for domain-specific models. Post-training of open-source models offers a more economical way to meet this growing demand, but the cost of accelerator infrastructure often pushes organizations to outsource the process to third-party providers. An untrusted provider may deviate from the declared training procedure to save computation or inject malicious behavior. A potential solution is to audit the training by having an independent verifier replay the training and compare the results. This faces two key challenges: benign numerical drift from floating-point computation across heterogeneous accelerators is difficult to distinguish from malicio...
| Authors | Hongxu Su, Jianzhu Yao, Huan Zhang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 19 June 2026 |
| Last updated at source | 29 September 2026 |
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