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  • Data Selection via Optimal Control for Language Models
    Based on these theoretical results, we introduce PMP-based Data Selection (PDS), a framework that approximates optimal data selection by solving the PMP conditions
  • Data Selection via Optimal Control for Language Models
    We formulate data selection as a generalized Optimal Control problem, which can be solved theoretically by Pontryagin's Maximum Principle (PMP), yielding a set of necessary conditions that characterize the relationship between optimal data selection and LM training dynamics
  • Data Selection via Optimal Control for Language Models
    Large amount of data makes pre-training quite inefficient High-quality pre-training data is running out Data selection cleaning is a heuristic-based tricky task
  • Data Selection via Optimal Control for Language Models
    Based on these theoretical results, we introduce PMP-based Data Selection (PDS), a framework that approximates optimal data selection by solving the PMP conditions
  • 论文笔记（2025. 07. 21）(ICLR 2025 oral) Data Selection via . . .
    这篇论文巧妙的从最优控制的视角审视Data Selection，用Pontryagin Maximum Principle的方法为Data Selection提供了新的解决方案，并通过实验验证了有效性，斩获了ICLR 2025的oral，无论是思路还是PMP工具的应用，都值得笔者写一篇blog记录一下。 顾名思义，就是在大规模数据集中识别并删除重复记录，以此提升存储效率和数据质量。 一般采取的方法有哈希、模糊匹配（比如通过Levenshtein距离判定）等方法，这些方法都比较好理解，也更接近人工去重的思维。
  • ICLR 2025 Oral | 训练LLM，不只是多喂数据，PDS框架给出 . . .
    前言 清华大学、北京大学联合微软亚洲研究院提出了 PMP-based Data Selection（PDS）方法，首次将数据选择建模为最优控制问题，基于庞特里亚金最大值原理（PMP）推导出理论条件，明确了“哪些数据更值得学”。
  • LMOps data_selection at main · microsoft LMOps · GitHub
    Solve data quality scores based on a small model (160M), small total steps (100), SGD, and small proxy data (163,840 samples) Fit the scores on the small proxy data with a 125M fairseq dense model (data scorer)
  • Data Selection via Optimal Control for Language Models
    A comprehensive review of existing literature on data selection methods and related research areas is presented, providing a taxonomy of existing approaches and drawing attention to noticeable holes in the literature
  • Data Selection via Optimal Control for Language Models
    The paper outlines how data selection can be approached using Optimal Control theory, where control variables (data points) in a dynamic system (pre-training process) are optimized to achieve desired outcomes (low downstream loss)
  • Data Selection via Optimal Control for Language Models
    This paper presents a novel framework called PMP-based Data Selection (PDS) that utilizes Optimal Control theory to efficiently select high-quality pre-training data for language models, significantly improving performance and reducing data demands across various model sizes and tasks





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