ICLHerd: Herding-Based Data Selection for LLMs with In-Context Learning as a Proxy

arXiv:2610.27142v1 Announce Type: cross Abstract: Data selection plays a central role in modern large language model training, as the quality of training data strongly influences downstream performance. A common approach is to curate a development set that encodes the desired skills (e.g., reasoning or multilinguality) and select data that are similar to them. Existing methods compute similarity using cosine distance over either the model's hidden states or gradients. However, we observe that this max-similarity strategy induces a mode-seeking bias: the selected data clusters tightly around th

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