3 papers
cs.LG2026
Learning to Reason in 13 Parameters
John X. Morris, Niloofar Mireshghallah, Mark Ibrahim +1
Recent research has shown that language models can learn to \textit{reason}, often via reinforcement learning. Some work even trains low-rank parameterizations for reasoning, but c…
cs.CL2025
Better Language Model Inversion by Compactly Representing Next-Token Distributions
Murtaza Nazir, Matthew Finlayson, John X. Morris +2
Language model inversion seeks to recover hidden prompts using only language model outputs. This capability has implications for security and accountability in language model deplo…
cs.CL2025
NeoBERT: A Next-Generation BERT
Lola Le Breton, Quentin Fournier, Mariam El Mezouar +2
Recent innovations in architecture, pre-training, and fine-tuning have led to the remarkable in-context learning and reasoning abilities of large auto-regressive language models su…