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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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3.1.2 Synthesizing for Target Skills

  1. How did Tülu 3 keep its synthetic prompts diverse?
    By persona-driven generation: GPT-4o was given one of about 250K personas (e.g. “a machine learning researcher focused on neural networks”) along with a request such as “create a coding problem”, which avoids the repetitive mode collapse of plain prompting. This produced math, coding and verifiable instruction-following data.
    • Lambert et al., 2024, Tülu 3 (arXiv preprint arXiv:2411.15124), §3.1.2

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