Re:Next-Concept Modeling: Fast, Parallel Coarse-to-Fine Text...
by johntb86 on Monday November 09, 2026 @11:53AM
(#50428163)
Maybe if it's worse than next-token-prediction it could still be used as a drafting model, similar to how DFlash works.
Re:Next-Concept Modeling: Fast, Parallel Coarse-to-Fine Text...
by cubefox on Monday November 09, 2026 @12:15PM
(#50428449)
What's that?
Re:Next-Concept Modeling: Fast, Parallel Coarse-to-Fine Text...
by johntb86 on Monday November 09, 2026 @12:54PM
(#50428960)
https://arxiv.org/abs/2602.06036 - they use a faster (single-pass) diffusion model to generate predictions for 7 to 15 tokens in a single step. Then if they're accepted that really speeds up the autoregressive model because it doesn't have to generate tokens one at a time.
Re:Next-Concept Modeling: Fast, Parallel Coarse-to-Fine Text...
by cubefox on Monday November 09, 2026 @10:57PM
(#50435879)
I'm pretty sure fast speculative decoding models need to have the same objective (NTP, MDM, NCM) as the larger model they try to accelerate.
Re:Next-Concept Modeling: Fast, Parallel Coarse-to-Fine Text...
by 3836293648 on Tuesday November 10, 2026 @04:54PM
(#50446838)
Just for additional context: You have to do more computational work to check them, beyond the extra work in the drafter, but you have much less of a memory bottleneck, which is extremely worth it on current hardware.
However, they hardly compare against the conventional next-token-prediction objective, which suggests they don't beat it, except for highly non-linear tasks like sudoku. Unfortunately, machine learning papers often tend to focus exclusively on the positives without mentioning bad aspects.