Hierarchical Data Selection via Manifold Coverage and Sparse Feature Coverage in LLM Post-training
MASS learns low-dimensional principal manifold coordinates with a dense autoencoder for coarse semantic grouping, and then performs quality-aware sparse feature coverage within each group using a TopK sparse autoencoder and proposes MASS.
Peng Sun, Yi Yang, Antong Zhang et al.
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