BLS approximates per-sample loss importance via EMA of batch losses, enabling simple and effective dynamic pruning of 20-50% samples losslessly across many datasets and models.
S., Daruwalla, K., and Lipasti, M
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
AlignPrune uses a Dynamic Alignment Score from loss trajectories to identify noisy samples more accurately than per-sample loss, improving pruning accuracy by up to 6.3% on noisy benchmarks.
Data Agent learns a co-evolving sample selection policy end-to-end that accelerates training by over 50% on ImageNet-1k and MMLU with no performance loss.
citing papers explorer
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Batch Loss Score for Dynamic Data Pruning
BLS approximates per-sample loss importance via EMA of batch losses, enabling simple and effective dynamic pruning of 20-50% samples losslessly across many datasets and models.
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Beyond Loss Values: Robust Dynamic Pruning via Loss Trajectory Alignment
AlignPrune uses a Dynamic Alignment Score from loss trajectories to identify noisy samples more accurately than per-sample loss, improving pruning accuracy by up to 6.3% on noisy benchmarks.
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Data Agent: Learning to Select Data via End-to-End Dynamic Optimization
Data Agent learns a co-evolving sample selection policy end-to-end that accelerates training by over 50% on ImageNet-1k and MMLU with no performance loss.