Post #1456

Hugging Face (Twitter) RT @rohanpaul_ai: A 7B model, tuned for forms and docs, beats giant models at pulling structured data. Beats GPT-4.1 on 1,000 extraction tasks, trained for $196. The team generated synthetic training data that preserves memory across chunks of a long file. That memory lets the model connect names, dates, and values that appear far apart. They fine-tuned with Low Rank Adaptation, changing only 0.53% of weights. They then used Group Relative Policy Optimization with a semantic reward and strict JSON checks. This setup accepts different surface wording if the meaning matches. On 1,000 held-out tasks it hit 0.573 mean reward and 89% valid JSON, trained for $196, ahead of GPT-4.1 and others. Result, a small focused model can outperform general models and cost much less. ---- Paper – arxiv. org/abs/2509.22906 Paper Title: "Extract-0: A Specialized Language Model for Document Information Extraction"