LangFlow: Continuous Diffusion Rivals Discrete in Language Modeling (Apr 2026)

Title: LangFlow: Continuous Diffusion Rivals Discrete in Language Modeling (Apr 2026)
Link: http://arxiv.org/abs/2604.11748v3
Date: April 2026

Summary:
LangFlow is the first continuous diffusion language model (DLM) to rival the performance of discrete diffusion counterparts. It connects embedding-space DLMs to Flow Matching via Bregman divergence and introduces three key innovations: a novel ODE-based negative log-likelihood (NLL) bound for principled evaluation, an 'information-uniform' noise scheduler based on a Gumbel distribution, and revised self-conditioning training protocols. The model achieves state-of-the-art results on benchmarks like LM1B and OpenWebText, providing clear evidence that continuous diffusion is a promising paradigm for language modeling.

Key Topics:
- Continuous Diffusion Language Models
- Flow Matching
- Bregman Divergence
- Information-Uniform Noise Scheduling
- ODE-based Perplexity Estimation
- Self-Conditioning

Chapters:
00:00 - Introduction to Langflow
01:11 - Challenges of Text Diffusion
02:30 - Discrete and Simplex Diffusion
03:32 - Embedding Space Path
04:43 - Bregman Flow Matching
06:03 - ODE Perplexity Estimation
07:43 - Information Uniform Principle
09:03 - Gumbel Noise Scheduling
10:11 - Rectified Self-Conditioning
11:40 - Benchmarks and Scaling
12:59 - Limitations and Future Outlook

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