AI paper argues best-of-K boosts generative expressivity, not just sampling quality
anshulkundaje · x · 2026-08-04
- The thread argues that the paper’s real contribution is not “best-of-K” itself, but the idea that a simple sampling-and-keep-best loop can increase generative expressivity without changing the model’s generation factorization.
- It says the method is framed differently from autoregression and diffusion: expressivity becomes a bottleneck as models and data scale, so adding exploration should help increasingly at larger scale.
- The post claims the experiments were designed to test that prediction, and that the results include hybrid XMs, exploration on top of diffusion/flow, jumpy, and MDLM pretraining.
- The image excerpt reinforces the same point: the paper’s novelty is the new view of what the loop does, not the idea of best-of-K itself.
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