8 citations · 10 across the 7 of their papers we have counts for
3 papers · 1 filter
Data Repetition Beats Data Scaling in Long-CoT Supervised Fine-Tuning
Dawid J. Kopiczko, Sagar Vaze, Tijmen Blankevoort +1
Supervised fine-tuning (SFT) on chain-of-thought data is an essential post-training step for reasoning language models. Standard machine learning intuition suggests that training w…
Bitune: Leveraging Bidirectional Attention to Improve Decoder-Only LLMs
Dawid J. Kopiczko, Tijmen Blankevoort, Yuki M. Asano
Decoder-only large language models typically rely solely on masked causal attention, which limits their expressiveness by restricting information flow to one direction. We propose…
VeRA: Vector-based Random Matrix Adaptation
Dawid J. Kopiczko, Tijmen Blankevoort, Yuki M. Asano
Low-rank adapation (LoRA) is a popular method that reduces the number of trainable parameters when finetuning large language models, but still faces acute storage challenges when s…