OVIG: Optimistic Verification of AI Training Integrity via Gradient Signals

תקציר מקורי באנגליתarXiv:2606.21045v2 Announce Type: replace-cross Abstract: 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 malicious deviations, and the checkpoints and metada