🧮 Terence Tao's blog is hosting a community hub to rewrite OpenAI's math write-ups
It's a guest post by Abhinav Nellore, Johannes Schmitt, Soledad Villar and Rachel Ward. They say OpenAI's published proofs are hard to follow and short on citations, so the hub on GitHub is recruiting students, postdocs and professors to revise them and to serve as reviewers.
It's a guest post by Abhinav Nellore, Johannes Schmitt, Soledad Villar and Rachel Ward. They say OpenAI's published proofs are hard to follow and short on citations, so the hub on GitHub is recruiting students, postdocs and professors to revise them and to serve as reviewers.
What's new
oai-math-revised: a community hub for revising OpenAI’s math write-ups
[This is a guest post by Abhinav Nellore, Johannes Schmitt, Soledad Villar, and Rachel Ward – T.] OpenAI’s math write-ups at can be difficult to understand and can have incomplete citat…
🚨 Stanford's Next-Concept Modeling writes text coarse-to-fine, in a handful of parallel steps
Hazy Research says NCM builds a hierarchy of discrete codes and predicts each level all at once. It claims quality that matches or beats strong diffusion language models, at 10-31x the speed and with 4-5x fewer training tokens.
The baselines are diffusion models, not autoregressive LLMs. Scaling is still unproven.
Hazy Research says NCM builds a hierarchy of discrete codes and predicts each level all at once. It claims quality that matches or beats strong diffusion language models, at 10-31x the speed and with 4-5x fewer training tokens.
The baselines are diffusion models, not autoregressive LLMs. Scaling is still unproven.
hazyresearch.stanford.edu
Next-Concept Modeling: Fast, Parallel Coarse-to-Fine Text Generation
Next-Concept Modeling (NCM) generates text from coarse concepts to fine details in a handful of parallel steps, matching or beating strong diffusion language models while running 10–31× faster and training on 4–5× fewer tokens.