Distribution Fine Tuning: A post-training step to make models write better
Distribution Fine Tuning (DFT) is introduced as a post-training method to improve language model writing quality. Unlike traditional fine-tuning that adjusts weights, DFT modifies the output probability distribution to align with desired writing styles or characteristics. This approach aims to make models produce more coherent and stylistically appropriate text without extensive retraining.