MixMin: Finding Data Mixtures via Convex Minimization

• URL: http://arxiv.org/abs/2502.10510v1
• Date: Fri, 14 Feb 2025 19:15:53 GMT
• Title: MixMin: Finding Data Mixtures via Convex Minimization
• Authors: Anvith Thudi, Evianne Rovers, Yangjun Ruan, Tristan Thrush, Chris J. Maddison,
• Abstract summary: Machine learning pipelines are increasingly combining and mixing data from diverse and disparate sources.<n>Finding the optimal data mixture is a challenging and open problem.<n>We formalize this data mixing problem as a bi-level objective: the best mixture is the one that would lead to the best model for a downstream objective.<n>In this paper, we make the observation that the bi-level data mixing objective becomes convex as our model class becomes larger.
• Score: 23.369015146176928
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern machine learning pipelines are increasingly combining and mixing data from diverse and disparate sources, e.g., pre-training large language models. Yet, finding the optimal data mixture is a challenging and open problem. We formalize this data mixing problem as a bi-level objective: the best mixture is the one that would lead to the best model for a downstream objective. Unfortunately, this objective is generally intractable. In this paper, we make the observation that the bi-level data mixing objective becomes convex as our model class becomes larger. We develop and study a gradient-based approach for optimizing this convex objective, which we call MixMin, and test it on language modeling and chemistry tasks. MixMin was the only method that uniformly improved the data mixture in all our experiments. With MixMin, we improved the data mixture using less than 0.2% additional compute for a pythia-410M model trained on 8.2B tokens, resulting between 1-5% relative improvement to negative log likelihood on PIQA, ARC Easy, SciQ, and OpenWebMath. Crucially, we found that MixMin mixtures for smaller models improved training of larger models, suggesting that MixMin mixtures may be scale-invariant. When mixing bioassay data to train an XGBoost model, we saw improvements to average precision scores of 0.03-0.15.

Related papers

• CLIMB: CLustering-based Iterative Data Mixture Bootstrapping for Language Model Pre-training [63.07024608399447]
  We propose an automated framework that discovers, evaluates, and refines data mixtures in a pre-training setting. We introduce ClimbLab, a filtered 1.2-trillion-token corpus with 20 clusters as a research playground, and ClimbMix, a compact yet powerful 400-billion-token dataset.
  arXiv  Detail & Related papers  (2025-04-17T17:58:13Z)
• Optimizing Pre-Training Data Mixtures with Mixtures of Data Expert Models [24.396525123797073]
  We propose a method to optimize language model pre-training data mixtures through efficient approximation of the cross-entropy loss corresponding to each candidate mixture. We use this approximation as a source of additional features in a regression model, trained from observations of model loss for a small number of mixtures.
  arXiv  Detail & Related papers  (2025-02-21T21:27:48Z)
• RegMix: Data Mixture as Regression for Language Model Pre-training [40.45464495981735]
  We propose RegMix to automatically identify a high-performing data mixture by formulating it as a regression task.<n>RegMix trains many small models on diverse data mixtures, uses regression to predict performance of unseen mixtures, and applies the best predicted mixture to train a large-scale model with orders of magnitude more compute.
  arXiv  Detail & Related papers  (2024-07-01T17:31:03Z)
• RC-Mixup: A Data Augmentation Strategy against Noisy Data for Regression Tasks [27.247270530020664]
  We study the problem of robust data augmentation for regression tasks in the presence of noisy data. C-Mixup is more selective in which samples to mix based on their label distances for better regression performance. We propose RC-Mixup, which tightly integrates C-Mixup with multi-round robust training methods for a synergistic effect.
  arXiv  Detail & Related papers  (2024-05-28T08:02:42Z)
• Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance [55.872926690722714]
  We study the predictability of model performance regarding the mixture proportions in function forms. We propose nested use of the scaling laws of training steps, model sizes, and our data mixing law. Our method effectively optimize the training mixture of a 1B model trained for 100B tokens in RedPajama.
  arXiv  Detail & Related papers  (2024-03-25T17:14:00Z)
• Efficient Online Data Mixing For Language Model Pre-Training [101.45242332613944]
  Existing data selection methods suffer from slow and computationally expensive processes. Data mixing, on the other hand, reduces the complexity of data selection by grouping data points together. We develop an efficient algorithm for Online Data Mixing (ODM) that combines elements from both data selection and data mixing.
  arXiv  Detail & Related papers  (2023-12-05T00:42:35Z)
• Tailoring Mixup to Data for Calibration [12.050401897136501]
  We show that the likelihood of assigning a wrong label with mixup increases with the distance between data to mix. We propose to dynamically change the underlying distributions of coefficients depending on the similarity between samples to mix.
  arXiv  Detail & Related papers  (2023-11-02T17:48:28Z)
• Learning with MISELBO: The Mixture Cookbook [62.75516608080322]
  We present the first ever mixture of variational approximations for a normalizing flow-based hierarchical variational autoencoder (VAE) with VampPrior and a PixelCNN decoder network. We explain this cooperative behavior by drawing a novel connection between VI and adaptive importance sampling. We obtain state-of-the-art results among VAE architectures in terms of negative log-likelihood on the MNIST and FashionMNIST datasets.
  arXiv  Detail & Related papers  (2022-09-30T15:01:35Z)
• DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classification [56.817386699291305]
  This paper proposes a simple yet effective data augmentation approach termed DoubleMix. DoubleMix first generates several perturbed samples for each training data. It then uses the perturbed data and original data to carry out a two-step in the hidden space of neural models.
  arXiv  Detail & Related papers  (2022-09-12T15:01:04Z)
• Harnessing Hard Mixed Samples with Decoupled Regularizer [69.98746081734441]
  Mixup is an efficient data augmentation approach that improves the generalization of neural networks by smoothing the decision boundary with mixed data. In this paper, we propose an efficient mixup objective function with a decoupled regularizer named Decoupled Mixup (DM) DM can adaptively utilize hard mixed samples to mine discriminative features without losing the original smoothness of mixup.
  arXiv  Detail & Related papers  (2022-03-21T07:12:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.