title: WILDCHAT-50M: A Deep Dive Into the Role of Synthetic Data in
    Post-Training
summary: 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.
paper_authors._id: 679c9419a01fd6df443d5729
paper_authors.name: Benjamin Feuer
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-31T09:35:53.653Z
paper_authors._id: 62f7f4efe7c1c9bf10c81465
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62f7f4efe7c1c9bf10c81465/AYlOg0fkP1o4GAP-8Y3xt.jpeg
paper_authors.fullname: Benjamin Feuer
paper_authors.type: user
paper_authors.user: penfever
paper_authors._id: 679c9419a01fd6df443d572a
paper_authors.name: Chinmay Hegde
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-03T16:07:36.155Z
paper_authors._id: 631620f6894404e25068856f
paper_authors.avatarUrl: /avatars/52c30caa0ee11347f82420a14ec19996.svg
paper_authors.fullname: Chinmay Hegde
paper_authors.type: user
paper_authors.user: chegde
paper_title: WILDCHAT-50M: A Deep Dive Into the Role of Synthetic Data in
    Post-Training
paper_summary: 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.
paper_ai_keywords: WILDCHAT-50M SFT mix RE-WILD Tulu-3