Continuous Latent Diffusion Language Model
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| Authors | Hongcan Guo et al. |
| Year | 2026 |
| HF Upvotes | 64 |
| arXiv | 2605.06548 |
| Download | |
| HF Page | View on Hugging Face |
Abstract
Large language models have achieved remarkable success under the autoregressive paradigm, yet high-quality text generation need not be tied to a fixed left-to-right order. Existing alternatives still struggle to jointly achieve generation efficiency, scalable representation learning, and effective global semantic modeling. We propose Cola DLM, a hierarchical latent diffusion language model that frames text generation through hierarchical information decomposition. Cola DLM first learns a stable text-to-latent mapping with a Text VAE, then models a global semantic prior in continuous latent space with a block-causal DiT, and finally generates text through conditional decoding. From a unified Markov-path perspective, its diffusion process performs latent prior transport rather than token-level observation recovery, thereby separating global semantic organization from local textual realization. This design yields a more flexible non-autoregressive inductive bias, supports semantic compression and prior fitting in continuous space, and naturally extends to other continuous modalities. Through experiments spanning 4 research questions, 8 benchmarks, strictly matched ~2B-parameter autoregressive and LLaDA baselines, and scaling curves up to about 2000 EFLOPs, we identify an effective overall configuration of Cola DLM and verify its strong scaling behavior for text generation. Taken together, the results establish hierarchical continuous latent prior modeling as a principled alternative to strictly token-level language modeling, where generation quality and scaling behavior may better reflect model capability than likelihood, while also suggesting a concrete path toward unified modeling across discrete text and continuous modalities.
Engineering Breakdown
Plain English
Cola DLM replaces the standard left-to-right autoregressive approach to text generation with a hierarchical latent diffusion model that decomposes the problem into three stages: learning a stable text-to-latent mapping via a Text VAE, modeling global semantic structure in continuous latent space using a block-causal Diffusion Transformer, and finally decoding back to text. The key finding is that this hierarchical decomposition achieves better joint performance on generation efficiency, representation learning scalability, and global semantic modeling compared to existing non-autoregressive alternatives.
Key Engineering Insight
By performing diffusion in a learned continuous latent space rather than at the token level, Cola DLM decouples semantic modeling from discrete vocabulary constraints, enabling block-causal attention patterns that capture global context without the computational constraints of full bidirectional attention or the quality loss of strict left-to-right generation.
Why It Matters for Engineers
For production teams, this matters because autoregressive decoding has fundamental latency and parallelization limits — you generate one token at a time. If latent diffusion can match autoregressive quality while enabling parallel token generation and more efficient sampling, it directly solves inference speed and cost problems. The VAE-based latent mapping also suggests easier fine-tuning and domain adaptation without retraining from scratch.
Research Context
Prior non-autoregressive and diffusion-based language models faced a three-way tradeoff: they were fast but lower quality, or they struggled to learn efficient representations, or they failed at global semantic coherence. Cola DLM advances the field by showing that hierarchical decomposition (VAE → latent diffusion → conditional decode) can resolve this tradeoff, enabling the first viable alternative to autoregressive generation that doesn't sacrifice quality for speed. This paves the way for a new class of language models optimized for inference throughput rather than sequential token generation.
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