REVIEW 9 cited by
Efficient Online Data Mixing For Language Model Pre-Training
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The data used to pretrain large language models has a decisive impact on a model's downstream performance, which has led to a large body of work on data selection methods that aim to automatically determine the most suitable data to use for pretraining. Existing data selection methods suffer from slow and computationally expensive processes, a problem amplified by the increasing size of models and of pretraining datasets. Data mixing, on the other hand, reduces the complexity of data selection by grouping data points together and determining sampling probabilities across entire groups. However, data mixing proportions are typically fixed before training and therefore cannot adapt to changing training dynamics. To address these limitations, we develop an efficient algorithm for Online Data Mixing (ODM) that combines elements from both data selection and data mixing. Based on multi-armed bandit algorithms, our online approach optimizes the data mixing proportions during training. Remarkably, our method trains a model that reaches the final perplexity of the next best method with 19\% fewer training iterations, and improves performance on the 5-shot MMLU benchmark by 1.9% relative accuracy, while adding negligible wall-clock time during pretraining.
Forward citations
Cited by 9 Pith papers
-
Smooth Scaling Laws Hide Stepwise Token Learning
Token loss trajectories follow localized sigmoids whose learning-time spectrum quantitatively reconstructs scaling-law derivatives on T, D, and M axes and enables faster training via distribution reshaping.
-
Explaining Data Mixing Scaling Laws
Under a shared-head/disjoint-tail assumption, multi-domain loss decomposes into a capacity-competition term c_i x_i^*(h)^{-b_i} plus a per-domain noise term A_i(Dh_i)^{-a_i}, and the fitted law extrapolates optimal mi...
-
Language Models Improve When Pretraining Data Matches Target Tasks
Ranking pretraining documents by similarity to benchmark training examples (BETR) yields consistent benchmark gains and a 2.1x compute multiplier over DCLM-Baseline.
-
The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text
A new 8TB openly-licensed text corpus trains 7B LLMs that are competitive with Llama 1/2, showing that performant models need not depend on unlicensed web data.
-
GRAPE: Optimize Data Mixture for Group Robust Multi-target Adaptive Pretraining
GRAPE uses a minimax group-DRO scheme to reweight both source domains and target tasks during pretraining, improving multi-task reasoning and low-resource language modeling.
-
Merge to Mix: Mixing Datasets via Model Merging
Merge to Mix shows that the performance of a parameter-averaged model predicts the performance of a model fine-tuned on any dataset mixture, enabling fast and accurate dataset mixture selection.
-
AutoMixAlign: Adaptive Data Mixing for Multi-Task Preference Optimization in LLMs
AutoMixAlign adaptively reweights or resamples task data during DPO training to match specialist-model losses, improving average performance on helpfulness, coding, and safety benchmarks compared to standard DPO and m...
-
MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning
Multi-domain RLVR data mixing, guided by a quadratic surrogate fitted to 11 pilot runs, improves a Qwen2-VL-2B model's out-of-distribution accuracy by about 5 points over uniform mixing.
-
Rethinking Data Mixture for Large Language Models: A Comprehensive Survey and New Perspectives
A survey organizing LLM data mixture methods into offline and online families, with a fine-grained taxonomy based on optimization frameworks.
Discussion (0). Continue with ORCID to comment.