Less is Enough: Synthesizing Diverse Data in LLM Feature Space with Sparse Autoencoders

arXiv:2602.10388v4 Announce Type: replace-cross Abstract: The diversity of post-training data is critical for effective downstream performance in large language models (LLMs). Many existing approaches to constructing post-training data quantify diversity using text-based metrics that capture linguistic variation, but such metrics provide only weak signals for the task-relevant features that determine downstream performance. In this work, we introduce Feature Activation Coverage (FAC) which measures data diversity in an interpretable feature space. Building upon this metric, we further propose

Source: arXiv cs.AI — read the full report at the original publisher.

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