Supervised Fine-Tuning
HoldTechniques
A standard training stage that updates a model using labeled example outputs.
Why it's here
Placed in Hold: 1 article(s) of evidence from 1 source(s), led by research-stage coverage, with 0 in the last 30 days. Confidence 24%. Low accumulated evidence, so it defaults conservatively pending more signal.
Evidence (1)
- 7Hugging Face Blog·6/3/2026researchDPO Cuts Text Degeneration in OCR Models Beyond Chatbots
Hugging Face Blog describes how Dharma-AI used Direct Preference Optimization (DPO) after supervised fine-tuning to reduce text degeneration in structured OCR models. Across tested model families, degeneration fell in every case, with an average reduction of 59.4% and a best-case drop of 87.6%. The article argues that DPO can use a model's own failure outputs as rejection pairs, extending preference optimization beyond chatbot alignment.