Direct Preference Optimization Cuts OCR Text Degeneration by Up to 88%
Direct Preference Optimization (DPO) applied as a second‑stage training after supervised fine‑tuning slashes text‑degeneration in OCR models, cutting average failure loops by 59% and up to 87.6% across model families. The approach uses the model's own rejected outputs as binary preference signals, proving DPO works beyond chatbot alignment.