[Submitted on 23 Jul 2026]
Title:Data Quality over Capacity: Internalizing Documents into LoRA Adapters for Closed-Book QA
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Abstract: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-document corpus, a larger jump than any architectural change. We confirm a capacity trend (rank must grow with corpus size) entangled with a coupling between rank and learning rate that we initially misdiagnosed. On a 15-document slice we add a real retrieval baseline: the internalized adapter (84.2% recall) beats a BM25-RAG pipeline with a base reader (58.9%) and even a realistic gold-chunk oracle (65.6%) at lower latency. We report the full arc, including three misdiagnoses, as a case study in debugging LLM training empirically.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.21861 [cs.CL]
(or arXiv:2607.21861v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.21861
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Joan Figuerola Hurtado [view email] [v1] Thu, 23 Jul 2026 23:14:59 UTC (12 KB)
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