Cross-Stack Validation of Language-Model Training: A Clinical Fine-Tuning Case Study
This paper demonstrates that independently implemented training stacks, specifically PyTorch and a Zig-based framework called numbat, can serve as effective differential oracles to validate large-scale clinical language model fine-tuning, successfully uncovering 17 previously missed faults—including critical data rendering mismatches and language-specific memory management issues—that single-stack development overlooked.