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Tülu 3: Pushing Frontiers in Open Language Model Post-Training

Lambert et al., 2024, Tülu 3 (arXiv preprint arXiv:2411.15124)

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1 Introduction

  1. What gap does Tülu 3 address?
    Post-training (instruction tuning, preference tuning, RL) had become central to frontier models, but its data and recipes were rarely published, and fully open recipes lagged on core skills such as MATH, GSM8K and IFEval. Tülu 3 releases models (on Llama 3.1 bases, 8B to 405B) together with all data, code, recipes and evaluation tools.
    • Lambert et al., 2024, Tülu 3 (arXiv preprint arXiv:2411.15124), §1

Source

  • Nathan Lambert, Jacob Morrison, Valentina Pyatkin, Shengyi Huang, Hamish Ivison, Faeze Brahman, Lester James V. Miranda, Alisa Liu, Nouha Dziri, Xinxi Lyu, Yuling Gu, Saumya Malik, Victoria Graf, Jena D. Hwang, Jiangjiang Yang, Ronan Le Bras, Oyvind Tafjord, Chris Wilhelm, Luca Soldaini, Noah A. Smith, Yizhong Wang, Pradeep Dasigi and Hannaneh Hajishirzi. 2024. Tülu 3: Pushing Frontiers in Open Language Model Post-Training. In arXiv preprint arXiv:2411.15124. https://arxiv.org/abs/2411.15124