Recent years have witnessed an explosive trend of scaling ego-centric human videos for robot manipulation, yet it remains unclear which data actually benefits dexterous manipulation. We present SiMDex, a similarity-based data mining framework that casts human data selection for VLA post-training in dexterous manipulation as a recommendation problem. For each robot demonstration, SiMDex runs a three-stage recall-ranking-re-ranking cascade over ~32M egocentric human samples: recall and ranking select a task-relevant subset in a morphology-agnostic action space, and re-ranking verifies it on pixels, with no change to VLA architecture or training. Against a strong baseline trained with an equal amount of randomly sampled human data, SiMDex uses only ~1.49M mined samples (<5% of the pool) yet improves the overall success rate from 47.7% to 61.7%, showing that selective curation outperforms indiscriminate data mixing.

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