Researchers use gradient attribution to locate data that causes safety degradation during fine-tuning
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DatologyAI and UIUC researchers propose SimpleLoRIF, a scalable gradient attribution method for identifying samples in fine-tuning data that appear harmless but weaken safety alignment. The method simplifies LoRIF and backpropagates only through the last four transformer blocks, embedding all 939,343 Tulu 3 samples in about 1 hour on 8 H100s. In emergent misalignment experiments, the gradients of unsafe code training samples align with the broad harmful behavior that appears after fine-tuning.
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- Follow the Gradient: Tracing Safety Degradation Back to Training Data
arXiv · 2026-10-27 · Deep reading
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- arXiv: Alignment, Deception & OversightSelectedFollowing the Gradient: Tracing Large Model Safety Degradation to Training Data
Researchers from DatologyAI and UIUC propose SimpleLoRIF, a scalable gradient attribution method for identifying seemingly harmless samples in fine-tuning data that weaken safety alignment. By simplifying LoRIF and backpropagating only through the last four transformer blocks, the method can embed all 939,343 Tulu 3 samples in about 1 hour on 8 H100s. In emergent misalignment experiments, the gradients of unsafe code training samples align with the broad harmful behaviors that emerge after fine-tuning, reaching an AUC of 0.865 on the fine-tuned Mistral model and 0.604 on Qwen. The resulting SafeTulu removes the 20% of samples in Tulu 3 most aligned with the gradients of unsafe responses, reducing the GCG attack success rate by up to 22% on Qwen, Llama and Olmo, outperforming LLM-as-a-judge filtering and existing gradient methods while largely preserving general capabilities.