Diffusion Language Models (DLMs)

Definition

Diffusion Language Models (DLMs) are a class of neural networks that generate text through an iterative denoising process, similar to how diffusion models work in image generation. Unlike traditional autoregressive language models that generate text sequentially from left to right, DLMs use bidirectional context and can generate multiple tokens in parallel through a masked prediction approach.

The core principle involves starting with corrupted or masked text and iteratively refining it through multiple denoising steps until coherent text emerges. This alternative paradigm to sequential generation offers unique advantages in terms of parallel processing, bidirectional understanding, and flexible generation dynamics.

While diffusion models revolutionized image generation through tools like Stable Diffusion and DALL-E, applying the diffusion paradigm to language has presented unique challenges due to the discrete nature of text. Recent advances (2024-2025), particularly in model conversion techniques and training methodologies, have made large-scale DLMs increasingly practical and performant.

AR vs. DLM: Key Differences

Key Insight: The fundamental difference lies in how models access context—AR models see only past tokens (causal), while DLMs see the entire sequence (bidirectional), enabling parallel generation but requiring different training dynamics.

How It Works

Diffusion Language Models operate through a fundamentally different process than autoregressive generation:

Training Process

1. Forward Diffusion (Corruption):

• Start with clean training text
• Progressively corrupt text by masking or replacing tokens
• Create multiple corruption levels from slightly to heavily masked
• Model learns the relationship between corrupted and clean text

2. Reverse Diffusion (Denoising):

• Train model to predict original tokens from corrupted versions
• Learn to denoise text at various corruption levels
• Develop understanding of bidirectional context
• Build capability to reconstruct coherent text from noise

3. Masked Prediction Objective:

• Unlike AR models where every token contributes to loss
• DLMs supervise only masked positions (typically ~50% of tokens)
• Requires different batch size and training dynamics
• Benefits from larger batch sizes than AR training

Training Insight: DLMs require approximately 2x larger batch sizes than AR models to achieve equivalent learning signal density, since only masked positions (~50% on average) contribute to the loss during training.

Generation Process

Parallel Generation:

1. Start with fully masked or random tokens
2. Model predicts probabilities for masked positions
3. Iteratively refine predictions over multiple steps
4. Use bidirectional context from entire sequence
5. Converge to coherent text through denoising

Key Differences from Autoregressive:

• Bidirectional attention: Can attend to tokens in both directions
• Parallel decoding: Generate multiple tokens simultaneously
• Iterative refinement: Multiple passes improve quality
• Non-sequential: Not constrained to left-to-right generation

Types

Direct Training DLMs

Models trained as diffusion models from initialization:

• Approach: Train from scratch with diffusion objective
• Challenge: Requires extensive data and compute for competitive performance
• Advantage: Purpose-built for diffusion dynamics
• Examples: Early experimental DLMs like SUNDAE, DiffusionBERT

Converted DLMs (A2D)

Models converted from autoregressive to diffusion through continual training:

A2D Conversion Process (e.g., Simple Continual Pretraining):

• Start from pretrained autoregressive checkpoint
• Replace causal attention mask with bidirectional mask
• Continue training with diffusion objective
• Preserve knowledge from AR pretraining while adding bidirectional capabilities

Notable Examples:

• RND1-Base: 30B parameters (3B active using MoE), converted from Qwen3-30BA3B with 500B tokens of continual pretraining
• Dream-7B: 7B parameter diffusion model with competitive performance on reasoning benchmarks
• LLaDA-8B: 8B parameter DLM demonstrating A2D conversion viability

Breakthrough: RND1-Base demonstrates that simple conversion methods can create state-of-the-art DLMs—it outperforms Dream-7B and LLaDA-8B across multiple benchmarks while using a straightforward Simple Continual Pretraining approach.

Advantages:

• Leverages mature AR infrastructure and training expertise
• Preserves factual knowledge from trillions of tokens of pretraining
• More efficient than training from scratch (reuses existing compute investment)
• Faster path to competitive performance (weeks vs. months)

Sparse Mixture-of-Experts DLMs

Combining diffusion with MoE architecture:

• Architecture: Multiple expert networks with selective activation
• Efficiency: Only subset of parameters active per inference
• Example: RND1 with 30B total but 3B active parameters
• Benefits: Scalable capacity with controlled computational costs

Hybrid Models

Models combining diffusion and autoregressive approaches:

• Architecture: Use both generation paradigms for different tasks
• Flexibility: Leverage strengths of each approach
• Applications: Complex generation tasks requiring both sequential and parallel processing

Real-World Applications

Text Generation

DLMs offer alternative approaches to text generation:

Creative Writing:

• Plot brainstorming: Generate multiple plot directions simultaneously, then iteratively refine the most promising paths
• Character dialogue refinement: Edit character speech in the middle of a scene while maintaining consistency with surrounding narrative
• Poetry composition: Create verses where rhyme and meter constraints span multiple lines bidirectionally
• Story editing: Revise any paragraph while ensuring coherence with both preceding and following text

Content Creation:

• Document editing workflows: Allow users to edit any section and regenerate surrounding context that adapts to changes
• Multi-lingual content: Generate translations that consider full sentence structure bidirectionally for more natural phrasing
• Technical documentation: Refine specific sections while maintaining consistency with terminology and style throughout the document
• Marketing copy iteration: Generate multiple variations of headlines/taglines in parallel, then refine top candidates

Research and Experimentation

DLMs serve as valuable research platforms:

Alternative Paradigms:

• Non-autoregressive research: Studying how parallel generation compares to sequential in tasks like summarization, translation, and code generation
• Controllable generation: Exploring constraint satisfaction across entire sequences (e.g., maintaining specific tone, style, or factual consistency)
• Infilling applications: Generating missing text spans in documents, code, or structured data with bidirectional context
• Multi-modal extensions: Investigating how diffusion approaches can integrate text with images, audio, or structured data

Model Conversion Research:

• A2D optimization: Experimenting with different conversion strategies—from simple SCP to complex multi-stage approaches
• Knowledge retention studies: Measuring how much factual knowledge from AR pretraining is preserved during conversion
• Domain-specific conversion: Adapting AR models trained on specialized domains (medical, legal, scientific) to diffusion paradigms
• Efficiency analysis: Comparing training costs and performance trade-offs between direct DLM training vs. A2D conversion

Efficient Inference Scenarios

Potential advantages in specific deployment contexts:

Parallel Processing:

• GPU-optimized inference: Hardware with massive parallelism (modern GPUs) can process multiple token predictions simultaneously
• Batch document processing: Process multiple documents with different completion requirements in a single batch
• Interactive editing systems: Real-time text refinement in collaborative document editors where multiple users edit simultaneously
• Latency-optimized applications: Trade AR sequential latency for DLM parallel generation in time-sensitive applications

Sparse MoE Benefits:

• Cost-effective serving: RND1-Base uses only 3B active parameters (10% of 30B total), reducing inference costs by 90%
• Edge deployment potential: Smaller active parameter count enables deployment on resource-constrained devices
• Multi-tenant systems: Serve multiple users efficiently by activating different expert combinations per request
• Quality-speed flexibility: Adjust number of denoising iterations to balance between generation quality and latency requirements

Deployment Insight: Sparse MoE DLMs like RND1 offer a unique advantage—large model capacity (30B parameters) with small active inference footprint (3B), enabling both powerful capabilities and efficient deployment.

Key Concepts

Bidirectional Context

Unlike causal AR models, DLMs process information from both directions:

• Full sequence access: Attend to entire sequence simultaneously
• Contextual understanding: Better capture of relationships across text
• Non-causal attention: No restriction to previous tokens only
• Holistic generation: Consider entire context during generation

Iterative Refinement

DLMs generate through multiple denoising steps:

• Progressive improvement: Quality increases over iterations
• Error correction: Multiple chances to fix inconsistencies
• Flexible stopping: Can trade iterations for quality/speed
• Controllable process: Adjust generation through iteration control

Masked Prediction

Core training objective for DLMs:

• Token masking: Predict masked positions in sequences
• Partial supervision: Not all tokens contribute to loss each step
• Varying mask ratios: Different masking levels during training
• Denoising objective: Learn to recover original text from corrupted versions

Model Conversion (A2D)

Transforming AR models to diffusion through continual training:

Simple Continual Pretraining (SCP):

1. Start from strong AR checkpoint
2. Replace causal mask with bidirectional mask
3. Continue pretraining with diffusion objective
4. Use learning rate warmup for stability

Layer-Specific Learning Rates:

• Higher rates for attention layers (adapt to bidirectional context)
• Lower rates for FFN/MLP layers (preserve factual knowledge)
• Prevents catastrophic forgetting
• Maintains AR pretraining benefits

Knowledge Preservation: Research shows factual associations in Transformers reside primarily in FFN/MLP layers. Using lower learning rates for these layers during A2D conversion preserves the vast knowledge from AR pretraining while allowing attention mechanisms to adapt to bidirectional processing.

Challenges and Limitations

Training Complexity

Scaling Inefficiency:

• Historically require more data passes than AR models
• Less mature training infrastructure and expertise
• Fewer established best practices and recipes
• Higher computational requirements for equivalent performance

Batch Size Dynamics:

• Different optimal batch sizes than AR training
• Only masked positions contribute to learning
• Requires larger batches for stable training
• More complex batch size tuning

Technical Challenges

Discrete Text Nature:

• Text tokens are discrete unlike continuous images
• Diffusion naturally suited for continuous spaces
• Requires adaptation for discrete token prediction
• More complex than image diffusion

Generation Quality:

• May require multiple iterations for high quality
• Potential for inconsistencies across iterations
• Balancing speed vs. quality trade-offs
• Less predictable than AR generation

Infrastructure Limitations

Maturity Gap:

• AR models benefit from years of infrastructure development
• Fewer tools and frameworks optimized for DLMs
• Less community experience and expertise
• Limited production deployment examples

Evaluation Challenges:

• Different generation paradigm complicates comparison
• Existing benchmarks designed for AR models
• Unclear how to fairly compare iterative vs. sequential generation
• Need for new evaluation methodologies

Recent Developments (2024-2025)

Large-Scale Models

RND1-Base (Radical Numerics, October 2025):

• Scale: 30B parameters (3B active) using sparse MoE architecture
• Training: Converted from Qwen3-30BA3B with 500B tokens of continual pretraining
• Performance: State-of-the-art among open DLMs—outperforms Dream-7B and LLaDA-8B on MMLU, ARC-C, RACE, BBH, GSM8K, and MBPP
• Achievement: Largest and most capable open-source diffusion language model to date
• Significance: First demonstration that A2D conversion scales effectively beyond 8B parameters

Research Impact: RND1's success proves that simple methods work at scale—its straightforward Simple Continual Pretraining approach matches or exceeds complex multi-stage conversion pipelines while being easier to implement and reproduce.

Key Innovations:

• Simple Continual Pretraining (SCP): Three-step conversion process replacing complex pipelines
• Layer-specific learning rates: Preserves factual knowledge while enabling bidirectional adaptation
• Optimized batch sizing: Empirically determined that DLMs need larger batches than AR models
• Open research: Complete transparency with training recipes, code, and model weights on Hugging Face

Conversion Techniques

A2D Advances:

• Simpler conversion methods matching complex pipelines
• Better understanding of critical batch sizes for DLMs
• Improved knowledge preservation during conversion
• Systematic studies of scaling factors and stability

Training Optimizations

Efficiency Improvements:

• Better batch size utilization strategies
• Layer-specific optimization approaches
• More efficient use of AR pretraining knowledge
• Reduced computational requirements for conversion

Future Trends

Scaling Beyond 30B

Current trajectory suggests continued scaling:

• 100B+ parameter DLMs: Next generation of large diffusion models
• Improved efficiency: Better training dynamics and resource usage
• Frontier performance: Competitive with largest AR models
• Production readiness: More mature deployment infrastructure

Enhanced Conversion Methods

Evolution of A2D techniques:

• Automated conversion: AI-assisted optimization of conversion process
• Faster adaptation: Reduced training needed for conversion
• Better knowledge retention: Preserving more AR capabilities
• Multi-stage refinement: Sophisticated progressive conversion

Hybrid Architectures

Combining strengths of different approaches:

• AR-DLM hybrids: Models switching between generation paradigms
• Task-specific modes: Different approaches for different tasks
• Adaptive generation: Dynamic selection of generation method
• Unified frameworks: Single model with multiple generation capabilities

Practical Applications

Growing deployment of DLMs:

• Specialized use cases: Applications leveraging parallel generation
• Research platforms: Tools for studying language generation
• Production systems: Real-world deployment of large DLMs
• Custom solutions: Domain-specific diffusion models

Academic Sources

Foundational Papers

• "Denoising Diffusion Probabilistic Models" - Ho et al. (2020) - Foundational diffusion model framework
• "Diffusion-LM Improves Controllable Text Generation" - Li et al. (2022) - Early application of diffusion to language
• "Scaling Diffusion Language Models via Adaptation from Autoregressive Models" - Gong et al. (2024) - A2D conversion methodology

Recent Advances (2025)

• "Dream 7B: Diffusion Large Language Models" - Ye et al. (preprint, 2025) - 7B parameter diffusion model
• "Exploring Diffusion Transformer Designs via Grafting" - Chandrasegaran et al. (preprint, 2025) - Architecture exploration for DLMs
• "Diffusion Beats Autoregressive in Data-Constrained Settings" - Prabhudesai et al. (preprint, 2025) - Comparative analysis

Model Conversion Research

• "Critical Batch Size Revisited: A Simple Empirical Approach to Large-Batch Language Model Training" - Merrill et al. (preprint, 2025) - Batch size optimization
• "Locating and Editing Factual Associations in GPT" - Meng et al. (2022) - Knowledge localization in transformers
• "RND1: Simple, Scalable AR-to-Diffusion Conversion" - Chandrasegaran et al., Radical Numerics (October 2025) - Introduction of Simple Continual Pretraining methodology

Conclusion

Diffusion Language Models represent an exciting alternative paradigm to traditional autoregressive language generation. While historically challenging to train at scale, recent advances (2024-2025) in model conversion techniques—particularly A2D approaches—have made large-scale DLMs increasingly practical and performant.

The release of RND1-Base in October 2025 as the largest open-source DLM demonstrates that diffusion models can scale effectively to 30B+ parameters while maintaining competitive performance. As training methodologies mature and infrastructure improves, DLMs may offer unique advantages for specific applications requiring parallel generation, bidirectional context, or alternative generation dynamics.

The future of language modeling likely includes both autoregressive and diffusion approaches, each leveraging their respective strengths for different tasks and deployment scenarios. Continued research in model conversion, training optimization, and hybrid architectures will expand the practical applications of diffusion language models across the AI landscape.

Learn more about related concepts: LLM (Large Language Models), Transformer, Mixture-of-Experts, Generative AI, and Foundation Models.