MDM-Prime-v2: Binary Encoding and Index Shuffling Enable Scaling of Diffusion Language Models

arXiv:2603.16077 · cs.LG · Submitted 2026-03-17 · Read on arXiv

cs.LG

Submitted: 2026-03-17

Updated: 2026-09-02

Terminology

• Transformer — 21× in this paper · explained in 2 episodes
• Benchmark — 19× in this paper · explained in 2 episodes
• Accuracy — 14× in this paper · explained in 1 episode
• Diffusion Model — 11× in this paper · explained in 8 episodes
• Diffusion Models — 10× in this paper · explained in 6 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Diffusion Process — 8× in this paper · explained in 2 episodes
• Embeddings — 8× in this paper · explained in 2 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Association for Computational Linguistics — 3× in this paper
• Generalization — 3× in this paper · explained in 13 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Transformer Architecture — 3× in this paper · explained in 2 episodes
• Empirical Methods in Natural Language Processing — 2× in this paper
• Fidelity — 2× in this paper · explained in 2 episodes
• Latent Representation — 2× in this paper · explained in 2 episodes
• Latent Representations — 2× in this paper · explained in 3 episodes
• Perplexity — 2× in this paper · explained in 3 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Scaling Laws — 2× in this paper · explained in 4 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Grouped Query Attention — 1× in this paper
• Loss Function — 1× in this paper · explained in 2 episodes
• Non-stationarity — 1× in this paper · explained in 2 episodes
• Temporal Reasoning — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
• LLaMA: Open and Efficient Foundation Language Models

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