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Denoising Hierarchical Representations: Joint Continuous Diffusion for Language Modeling

Paper recorded by Signals 4 on 2026-10-06 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-06 on arXiv · recorded by Signals 4 on 2026-10-07

Category: cs.LG · 机器学习 · first seen 2026-10-07

Abstract

Diffusion Language Models (DLMs) hold the promise of order-agnostic, parallel text generation. Recently, continuous diffusion and flow matching models have seen substantial gains, driven by carefully crafted token representations and diffusion/flow spaces. In this work, we introduce Hierarchical Continuous Diffusion Language Models (H-CDLMs), a simple framework that further improves continuous DLM

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#363 most recent of 762 cs.LG papers we have recorded · ↑ newer: On the Computational Tractability of Robust Bandits · ↓ older: Optimal and Efficient Online Inverse Optimization
Cite this page: Denoising Hierarchical Representations: Joint Continuous Diffusion for Language Modeling: the #363 most recent of 762 cs.LG papers we have recorded (as of 2026-10-06). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/denoising-hierarchical-representations-joint-continuous-diffusion-for-language-m.html
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