WILDCHAT-50M: A Deep Dive Into the Role of Synthetic Data in
  Post-Training
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.
679c9419a01fd6df443d5729
Benjamin Feuer
admin_assigned
2025-01-31T09:35:53.653Z
62f7f4efe7c1c9bf10c81465
https://cdn-avatars.huggingface.co/v1/production/uploads/62f7f4efe7c1c9bf10c81465/AYlOg0fkP1o4GAP-8Y3xt.jpeg
Benjamin Feuer
user
penfever
679c9419a01fd6df443d572a
Chinmay Hegde
claimed_verified
2025-03-03T16:07:36.155Z
631620f6894404e25068856f
/avatars/52c30caa0ee11347f82420a14ec19996.svg
Chinmay Hegde
user
chegde
WILDCHAT-50M: A Deep Dive Into the Role of Synthetic Data in
  Post-Training
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.
The WILDCHAT-50M dataset, comprising responses from over 50 open-weight models, enables large-scale comparative analysis and demonstrates improved performance in SFT mixtures with fewer samples.
post-training DPO distillation synthetic data generating models LLM judges WildChat dataset parameter-efficient RE-WILD SFT mix Tulu-3 SFT mixture