MDM distills vision-language datasets via joint embedding clustering, weight-space model interpolation, and geometry-aware distribution matching on the unit hypersphere.
Selection via proxy: Efficient data se- lection for deep learning.arXiv preprint arXiv:1906.11829
6 Pith papers cite this work. Polarity classification is still indexing.
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POES frames prompt evaluation as online adaptive testing and uses a provably submodular objective to pick informative examples, delivering 6.2% higher average accuracy and 35-60% token savings versus naive full-set scoring.
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.
LFM models exhibit stability to data reduction and capacity shrinkage that is tied to the flow matching objective, enabling reduced-data training and coarse-to-fine inference with over 2x speedup.
A curriculum sampling questions with high variance in success rate improves reinforcement learning performance for LLM reasoning tasks.
Step-Video-T2V describes a 30B-parameter text-to-video model with custom Video-VAE, 3D DiT, flow matching, and Video-DPO that claims state-of-the-art results on a new internal benchmark.
citing papers explorer
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Learning to Reason at the Frontier of Learnability
A curriculum sampling questions with high variance in success rate improves reinforcement learning performance for LLM reasoning tasks.
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Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model
Step-Video-T2V describes a 30B-parameter text-to-video model with custom Video-VAE, 3D DiT, flow matching, and Video-DPO that claims state-of-the-art results on a new internal benchmark.