AdaSTaR: Adaptive Data Sampling for Training Self-Taught Reasoners

• URL: http://arxiv.org/abs/2505.16322v1
• Date: Thu, 22 May 2025 07:24:11 GMT
• Title: AdaSTaR: Adaptive Data Sampling for Training Self-Taught Reasoners
• Authors: Woosung Koh, Wonbeen Oh, Jaein Jang, MinHyung Lee, Hyeongjin Kim, Ah Yeon Kim, Joonkee Kim, Junghyun Lee, Taehyeon Kim, Se-Young Yun,
• Abstract summary: Self-Taughters (STaR) is an integral part of the training pipeline of self-improving reasoning Language Models (LMs)<n>We introduce Adaptive STaR (AdaSTaR), a novel algorithm that rectifies this by integrating two adaptive sampling principles.<n>AdaSTaR achieves best test accuracy in all instances and reduces training FLOPs by an average of 58.6% against an extensive list of baselines.
• Score: 19.27201880632717
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-Taught Reasoners (STaR), synonymously known as Rejection sampling Fine-Tuning (RFT), is an integral part of the training pipeline of self-improving reasoning Language Models (LMs). The self-improving mechanism often employs random observation (data) sampling. However, this results in trained observation imbalance; inefficiently over-training on solved examples while under-training on challenging ones. In response, we introduce Adaptive STaR (AdaSTaR), a novel algorithm that rectifies this by integrating two adaptive sampling principles: (1) Adaptive Sampling for Diversity: promoting balanced training across observations, and (2) Adaptive Sampling for Curriculum: dynamically adjusting data difficulty to match the model's evolving strength. Across six benchmarks, AdaSTaR achieves best test accuracy in all instances (6/6) and reduces training FLOPs by an average of 58.6% against an extensive list of baselines. These improvements in performance and efficiency generalize to different pre-trained LMs and larger models, paving the way for more efficient and effective self-improving LMs.

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