AutoMixer: Checkpoint Artifacts as Automatic Data Mixers

• URL: http://arxiv.org/abs/2506.21910v2
• Date: Fri, 01 Aug 2025 14:57:07 GMT
• Title: AutoMixer: Checkpoint Artifacts as Automatic Data Mixers
• Authors: Ernie Chang, Yang Li, Patrick Huber, Vish Vogeti, David Kant, Yangyang Shi, Vikas Chandra,
• Abstract summary: In this work, we observe that checkpoint models exhibit emerging capabilities at different points in the training trajectory.<n>We identify these artifact models based on their respective capabilities on the benchmarks.<n>We leverage them as data mixers by using their aggregated first-order influence approximation over source data.
• Score: 21.941385659504906
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In language model training, it is desirable to equip models with capabilities from various tasks. However, it is not clear how to directly obtain the right data mixtures for these capabilities as the relationship between data and tasks is difficult to be modeled. In this work, we observe that checkpoint models exhibit emerging capabilities at different points in the training trajectory. Often, the training process saves checkpoints as artifacts that are under-utilized as a source of in-training data signals. We identify these artifact models based on their respective capabilities on the benchmarks and leverage them as data mixers by using their aggregated first-order influence approximation over source data. We demonstrated on eight reasoning benchmarks that the proposed framework shows significant improvements in the pretraining setting, with performance improvements of up to 1.93%. Overall, this shows the potential of checkpoint models to enhance data quality and optimize data mixtures.

Related papers

• Approximating Language Model Training Data from Weights [70.08614275061689]
  We formalize the problem of data approximation from model weights and propose several baselines and metrics.<n>We develop a gradient-based approach that selects the highest-matching data from a large public text corpus.<n>Even when none of the true training data is known, our method is able to locate a small subset of public Web documents.
  arXiv  Detail & Related papers  (2025-06-18T15:26:43Z)
• Mixtera: A Data Plane for Foundation Model Training [1.797352319167759]
  We build and present Mixtera, a data plane for foundation model training.<n>We show that Mixtera does not bottleneck training and scales to 256 GH200 superchips.<n>We also explore the role of mixtures for vision-language models.
  arXiv  Detail & Related papers  (2025-02-27T05:55:44Z)
• Evaluating Sample Utility for Efficient Data Selection by Mimicking Model Weights [11.237906163959908]
  Multimodal models are trained on large-scale web-crawled datasets.<n>These datasets often contain noise, bias, and irrelevant information.<n>We propose an efficient, model-based approach using the Mimic Score.
  arXiv  Detail & Related papers  (2025-01-12T04:28:14Z)
• What Do Learning Dynamics Reveal About Generalization in LLM Reasoning? [83.83230167222852]
  We find that a model's generalization behavior can be effectively characterized by a training metric we call pre-memorization train accuracy. By connecting a model's learning behavior to its generalization, pre-memorization train accuracy can guide targeted improvements to training strategies.
  arXiv  Detail & Related papers  (2024-11-12T09:52:40Z)
• Dynamic Data Mixing Maximizes Instruction Tuning for Mixture-of-Experts [20.202031878825153]
  We propose a novel dynamic data mixture for MoE instruction tuning. Inspired by MoE's token routing preference, we build dataset-level representations and then capture the subtle differences among datasets. Results on two MoE models demonstrate the effectiveness of our approach on both downstream knowledge & reasoning tasks and open-ended queries.
  arXiv  Detail & Related papers  (2024-06-17T06:47:03Z)
• 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.<n>We propose nested use of the scaling laws of training steps, model sizes, and our data mixing law.<n>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)
• DsDm: Model-Aware Dataset Selection with Datamodels [81.01744199870043]
  Standard practice is to filter for examples that match human notions of data quality. We find that selecting according to similarity with "high quality" data sources may not increase (and can even hurt) performance compared to randomly selecting data. Our framework avoids handpicked notions of data quality, and instead models explicitly how the learning process uses train datapoints to predict on the target tasks.
  arXiv  Detail & Related papers  (2024-01-23T17:22:00Z)
• Dataless Knowledge Fusion by Merging Weights of Language Models [47.432215933099016]
  Fine-tuning pre-trained language models has become the prevalent paradigm for building downstream NLP models.<n>This creates a barrier to fusing knowledge across individual models to yield a better single model.<n>We propose a dataless knowledge fusion method that merges models in their parameter space.
  arXiv  Detail & Related papers  (2022-12-19T20:46:43Z)
• SynBench: Task-Agnostic Benchmarking of Pretrained Representations using Synthetic Data [78.21197488065177]
  Recent success in fine-tuning large models, that are pretrained on broad data at scale, on downstream tasks has led to a significant paradigm shift in deep learning. This paper proposes a new task-agnostic framework, textitSynBench, to measure the quality of pretrained representations using synthetic data.
  arXiv  Detail & Related papers  (2022-10-06T15:25:00Z)
• No One Representation to Rule Them All: Overlapping Features of Training Methods [12.58238785151714]
  High-performing models tend to make similar predictions regardless of training methodology. Recent work has made very different training techniques, such as large-scale contrastive learning, yield competitively-high accuracy. We show these models specialize in generalization of the data, leading to higher ensemble performance.
  arXiv  Detail & Related papers  (2021-10-20T21:29:49Z)
• It's the Best Only When It Fits You Most: Finding Related Models for Serving Based on Dynamic Locality Sensitive Hashing [1.581913948762905]
  Preparation of training data is often a bottleneck in the lifecycle of deploying a deep learning model for production or research. This paper proposes an end-to-end process of searching related models for serving based on the similarity of the target dataset and the training datasets of the available models.
  arXiv  Detail & Related papers  (2020-10-13T22:52:13Z)
• How Training Data Impacts Performance in Learning-based Control [67.7875109298865]
  This paper derives an analytical relationship between the density of the training data and the control performance. We formulate a quality measure for the data set, which we refer to as $rho$-gap. We show how the $rho$-gap can be applied to a feedback linearizing control law.
  arXiv  Detail & Related papers  (2020-05-25T12:13:49Z)

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.