WARP recovers training domain mixtures from fine-tuned model weights using weight-space interpolation via model merging to generate pseudo-checkpoints and geometric features mapped to proportions.
R&b: Domain regrouping and data mixture balancing for efficient foundation model training
2 Pith papers cite this work. Polarity classification is still indexing.
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A survey that taxonomizes data mixing strategies for LLM pretraining into static rule-based, learning-based, and dynamic adaptive families while highlighting transferability challenges and evaluation gaps.
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WARP: Weight-Space Analysis for Recovering Training Data Portfolios
WARP recovers training domain mixtures from fine-tuned model weights using weight-space interpolation via model merging to generate pseudo-checkpoints and geometric features mapped to proportions.
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Data Mixing for Large Language Models Pretraining: A Survey and Outlook
A survey that taxonomizes data mixing strategies for LLM pretraining into static rule-based, learning-based, and dynamic adaptive families while highlighting transferability challenges and evaluation gaps.