New @AIatMeta paper explains when a smaller, curated dataset beats using everything. Standard training wastes effort because many examples are redundant or wrong. They formalize a label generator, a pruning oracle, and a learner. From this, they derive exact error laws and sharp regime switches. With a strong generator and plenty of data, keeping hard examples works best. With a weak generator or small data, keeping easy examples or keeping more helps. They analyze 2 modes, label agnostic by features and label aware that first filters wrong labels. ImageNet and LLM math results match the theory, and pruning also prevents collapse in self training. ---- Paper – arxiv. org/abs/2511.03492 Paper Title: "Why Less is More (Sometimes): A Theory of Data Curation"

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Lots of discussion around JEPA and why latent space prediction works better than input space (e.g., LLMs) for certain modalities. But no one has formalized WHY. The answer lies in whether statistically dominant features are semantically meaningful. @NeurIPSConf spotlight 🧵👇