Any centered 1-subgaussian random vector equals the sum of a universal number of standard Gaussians, solving Talagrand's convexity conjecture.
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Dr. Post-Training reframes general data as a data-induced regularizer for LLM post-training updates, yielding a family of methods that outperform data-selection baselines on SFT, RLHF, and RLVR tasks.
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On Talagrand's Convexity Conjecture
Any centered 1-subgaussian random vector equals the sum of a universal number of standard Gaussians, solving Talagrand's convexity conjecture.
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Dr. Post-Training: A Data Regularization Perspective on LLM Post-Training
Dr. Post-Training reframes general data as a data-induced regularizer for LLM post-training updates, yielding a family of methods that outperform data-selection baselines on SFT, RLHF, and RLVR tasks.