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cs.LG2026
Self-Mined Hardness for Safety Fine-Tuning
Prakhar Gupta, Garv Shah, Donghua Zhang
Safety fine-tuning of language models typically requires a curated adversarial dataset. We take a different approach: score each candidate prompt's difficulty by how often the targ…
cs.LG2026
Improving Sparse Memory Finetuning
Satyam Goyal, Anirudh Kanchi, Garv Shah +1
Large Language Models (LLMs) are typically static after training, yet real-world applications require continual adaptation to new knowledge without degrading existing capabilities.…