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Collecting high-quality training examples for language model fine-tuning is expensive, with practical budgets limiting the amount of data that can be procured.
On the power of curriculum learning in training deep networks
Guy Hacohen and Daphna Weinshall · 1904
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Deep reinforcement learning from human preferences
Paul F. Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Data shapley: Equitable valuation of data for machine learning
Amirata Ghorbani and James Zou · 2019
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An empirical study of example forgetting during deep neural network learning
Mariya Toneva, Alessandro Sordoni, Rémi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J. Gordon · 2019
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Training verifiers to solve math word problems
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Deep learning on a data diet: Finding important examples early in training
Mansheej Paul, Surya Ganguli, and Gintare Karolina Dziugaite · 2021
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Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, and Ryan Lowe · 2022
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V. Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 2022
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Unsloth, 2023
Michael Han Daniel Han and Unsloth team · 2023
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Challenging big-bench tasks and whether chain-of-thought can solve them
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Marah Abdin, Jyoti Aneja, Harkirat Behl, Sébastien Bubeck, Ronen Eldan, Suriya Gunasekar, Michael Harrison, Russell J. Hewett, Mojan Javaheripi, Piero Kauffmann, James R. Lee, Yin Tat Lee, Yuanzhi Li, Weishung Liu, Caio C. T. Mendes, Anh Nguyen, Eric Price, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Xin Wang, Rachel Ward, Yue Wu, Dingli Yu, Cyril Zhang, and Yi Zhang · 2024
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Sparq: Synthetic problem generation for reasoning via quality-diversity algorithms, 2025
Alex Havrilla, Edward Hughes, Mikayel Samvelyan, and Jacob Abernethy · 2025
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Understanding r1-zero-like training: A critical perspective
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Aime2025 dataset
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Process reinforcement through implicit rewards
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Grad-match: Gradient matching based data subset selection for efficient deep model training
Krishnateja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, Abir De, and Rishabh Iyer
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Glister: Generalization based data subset selection for efficient and robust learning
KrishnaTeja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, and Rishabh K. Iyer
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