kalomaze on Label Asymmetry: Models Learn Conditioning Only When Forced To
kalomaze · x · 2026-09-21
kalomaze offers a representation-learning observation: when there's extreme asymmetry between labels and conditioning, how much the model learns about the conditioning is a function of how much predicting the labels well forces it to model the conditioning. His extreme example: BCE gender classification of human speech. He adds that language gets SSL on everything for free via NTP since there's no "these tokens are conditioning only" asymmetry — and jokes that one mutual runs NVL72s while another runs a 4090 + a dream, hinting that cleverness can offset compute gaps.
Related event: Developer Says Multimodal Training Still Needs SSL Backbones(4 posts)→
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