S-FLM is a hyperspherical latent flow language model that improves continuous flow language models on large-vocabulary reasoning tasks and closes the gap to masked diffusion at standard sampling temperature.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
Fast-dLLM adds reusable KV cache blocks and selective parallel decoding to diffusion LLMs, closing most of the speed gap with autoregressive models without retraining.
citing papers explorer
-
Language Modeling with Hyperspherical Flows
S-FLM is a hyperspherical latent flow language model that improves continuous flow language models on large-vocabulary reasoning tasks and closes the gap to masked diffusion at standard sampling temperature.
-
Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decoding
Fast-dLLM adds reusable KV cache blocks and selective parallel decoding to diffusion LLMs, closing most of the speed gap with autoregressive models without retraining.