WILDCHAT-50M: A Deep Dive Into the Role of Synthetic Data in
  Post-Training
Authors: name: Benjamin Feuer, name: Chinmay Hegde

Language model (LLM) post-training, from DPO to distillation, can refine
behaviors and unlock new skills, but the open science supporting these
post-training techniques is still in its infancy. One limiting factor has been
the difficulty of conducting large-scale comparative analyses of synthetic data
generating models and LLM judges. To close this gap, we introduce WILDCHAT-50M,
the largest public chat dataset to date. We extend the existing WildChat
dataset to include responses not only from GPT, but from over 50 different
open-weight models, ranging in size from 0.5B to 104B parameters. We conduct an
extensive comparative analysis and demonstrate the potential of this dataset by
creating RE-WILD, our own public SFT mix, which outperforms the recent Tulu-3
SFT mixture from Allen AI with only 40% as many samples. Our dataset, samples
and code are available at https://github.com/penfever/wildchat-50m.