3 papers
cs.CL2026
Sparse Memory Finetuning as a Low-Forgetting Alternative to LoRA and Full Finetuning
Prakhar Gupta, Garv Shah, Satyam Goyal +1
Adapting a pretrained language model to a new task often hurts the general capabilities it already had, a problem known as catastrophic forgetting. Sparse Memory Finetuning (SMF) t…
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.…