Conditional independence limits parallel token generation in masked diffusion models
alec_helbling · x · 2026-09-05
A technical thread explains masked diffusion models' core limitation: tokens sampled in the same step are conditionally independent, so each marginal can look sensible while the joint sequence is incoherent (e.g. "Alice won after Alice resigned"). Structured tasks like Sudoku therefore need few interdependent tokens per pass, limiting parallel-generation speedups.
More from Models
- Gemini somehow can't read its own Personal Instructions, user's toggles don't help — Connect_Reporter_799 · 2026-10-23
- Diffusion LLM bias rankings shift with sampling configs, persist after debiasing — Bollegala · 2026-10-23
- Running ~100 DeepSeek v4.1 flash agents in parallel for under $20/month — zack_overflow · 2026-10-23
- Researcher laments open-source audio models are surprisingly subpar, even representation ones — gowthami_s · 2026-10-23
- User claims they generated video with "Claude Opus 5.5", unannounced model name surfaces — jwt0625 · 2026-10-23
- Security researchers push back as OpenAI restricts Astra access; BlackHat 2027 mocked as 'cyber model' bazaar — cyb3rops · 2026-10-23