Data Quality over Capacity: Internalizing Documents into LoRA Adapters for Closed-Book QA
We study baking documents directly into the weights of a 4-bit Gemma-4-e4b model via LoRA, so a system can answer questions about a corpus closed-book: no retrieval and no context-window budget. Across roughly 100 training runs from single documents to a 99-document corpus, we find that once adapter capacity is adequate, training-data quality is the dominant lever on closed-book accuracy, outweighing LoRA rank, learning rate, and two alternative architectures combined; capacity itself is a hard gate below which no data intervention helps. A single curation pass (shortening gold answers to canonical 1-6 word spans and dropping trivia) moved closed-book accuracy from 57.7% to 85.7% on a 15-...
| Authors | Joan Figuerola Hurtado |
|---|---|
| Primary category | cs.CL |
| Published | 23 July 2026 |
| Last updated at source | 23 July 2026 |
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