Finetuning olmOCR to be a faithful OCR-Engine

finetuning olmOCR is fine, but the goal needs to be brutally specific: faithfulness to the source. we're not just looking for higher accuracy on a benchmark; we're looking for an OCR engine that performs reliably across different noise levels and complex layouts. the challenge with fine-tuning models like this is ensuring the output remains grounded and doesn't hallucinate characters or misinterpret spatial relationships.

if the fine-tuning doesn't robustly improve fidelity, we've just added another layer of complexity that costs us QA time. the system needs to handle edge cases reliably, especially when dealing with poor-quality source documents. honestly, without rigorous, high-fidelity testing against real-world document sets, this is just a glorified tweak.