I transformed the Plush interpreter from a stack-based design to a register-based architecture, and the performance boost was more than I expected!
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@Love2Code
💖 ➞ λ: CS PhD, into compiler design, programming languages, music, simulation, ML/AI, robotics. Follow me code code reviews, stock picks and dating advice.
Albert Gu @_albertguI've always believed that intelligence should come from simple, general building blocks that learn their own abstractions directly from raw data; in other words, the bitter lesson. I think we're finally seeing a real step towards AI that can learn from all data. Language modeling on raw bytes is a microcosm of this ideal, and to me it's a central problem in AI: if we can't build LLMs that learn end-to-end from bytes, how will we handle the rest of the world's data that we understand even less? Last year we introduced hierarchical networks (H-Nets) as a first step. Our new version finds up to 3-5x efficiency gains in controlled scaling law experiments, and at larger scale matches an open-source model trained on 11T tokens with ~10x less data and compute. The posts below walk through what we found and why we think it works.