RAG Retrieves. Fine-Tuning Forgets. HyperNetworks Inject - and Now We Have the Scaling Laws.

There are two mainstream answers to "how do I get my LLM to reliably use proprietary data?" RAG retrieves relevant chunks at inference time. Fine-tuning bakes new knowledge into the weights. Both are widely deployed. Both have well-documented failure modes.

A new paper from Nace AI and Purdue University proposes a third path backed by the first systematic scaling laws: HyperNetwork-based knowledge injection. Instead of modifying the LLM, you train a second network that generates LoRA adapters from a batch of facts. The base model never changes.

Why Fine-Tuning Is a Harder Problem Than It Looks

Catastrophic forgetting. Updating weights on new facts degrades existing capabilities.

OOD generalization failure. Fine-tuned models struggle with new entity combinations not seen during training.

RAG Retrieves. Fine-Tuning Forgets. HyperNetworks Inject - and Now We Have the Scaling Laws.

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