Yacine: pufferlib baselines train in 5 seconds, perfect for fast architecture sweep experiments
yacineMTB · x · 2026-10-28
Responding to a discussion on whether neural networks can naturally grow modular "computer" structures, Yacine suggests trying pufferlib baselines: the models train in literally 5 seconds, so you can run weird architecture experiments and sweep to quickly see whether an idea works.
His interlocutor is skeptical, arguing it's a fundamental information-theoretic problem — slopping RL tasks or procedural data won't make circuits self-organize into von Neumann-style architectures.
Related event: Debate: Can Neural Nets Spontaneously Emerge Modular Structures?(3 posts)→
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