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Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance
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Pretraining data of large language models composes multiple domains (e.g., web texts, academic papers, codes), whose mixture proportions crucially impact the competence of outcome models. While existing endeavors rely on heuristics or qualitative strategies to tune the proportions, we discover the quantitative predictability of model performance regarding the mixture proportions in function forms, which we refer to as the data mixing laws. Fitting such functions on sample mixtures unveils model performance on unseen mixtures before actual runs, thus guiding the selection of an ideal data mixture. Furthermore, we propose nested use of the scaling laws of training steps, model sizes, and our data mixing law to enable predicting the performance of large models trained on massive data under various mixtures with only small-scale training. Moreover, experimental results verify that our method effectively optimizes the training mixture of a 1B model trained for 100B tokens in RedPajama, reaching a performance comparable to the one trained for 48% more steps on the default mixture. Extending the application of data mixing laws to continual training accurately predicts the critical mixture proportion that avoids catastrophic forgetting and outlooks the potential for dynamic data schedules
Forward citations
Cited by 7 Pith papers
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Moving Alphabet: A Controlled Study of Training Data for Text-to-Video Generation
Using a synthetic alphabet video testbed, balanced data mixing and caption precision are shown to dominate T2V model quality, while CFG and fine-tuning only partially compensate for corrupted captions.
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Domain-Aware Scaling Laws Uncover Data Synergy
Domain-aware scaling laws with fitted γ and σ synergy terms recover stable code-math interactions from observational LLM mixtures and correctly predict mixture rankings in controlled small-scale trainings.
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Co-Adaptive Multi-Task LoRA: Transfer-Aware, Label-Free Control of Domain Participation
A forward-only controller sets multi-domain LoRA participation from label-free competence and cross-domain affinity, improving average accuracy while using half the data.
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The Kinetics of Training: A Driven-Nucleation Rate Law for Emergence, Plasticity Loss, and Circuit Control in Language Models
Capability formation in small transformers is claimed to obey a driven-nucleation rate law J = Nνσ(c)e^{−βK} − D, read forward as emergence, backward as plasticity loss, and completed as circuit control.
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GradAlign: Gradient-Aligned Data Selection for LLM Reinforcement Learning
GradAlign selects RL training problems whose policy gradients align with validation-set gradients, claiming more stable and better-performing LLM post-training.
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Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training
Domain2Vec finds better LLM pretraining data mixtures by aligning, in a training-free way, the meta-domain distribution of the training set with the validation set's distribution.
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Using Scaling Laws for Data Source Utility Estimation in Domain-Specific Pre-Training
Running multiple short annealing runs at different token scales can reveal per-source utility scaling curves that change data-source rankings compared with single point estimates.
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