Diffusion as a Training Curriculum for Timestep-Free Iterative Reasoning
Diffusion models and recursive reasoners are both iterative, but they carry information across iterations differently. We add a persistent hidden state to a diffusion denoiser and remove its timestep conditioning, leaving a single shared update that can be run to arbitrary depth. The result is an anytime solver: accuracy keeps improving with inference depth far beyond the rollout lengths and backpropagation window used in training, reaching 99.90% exact solve on Sudoku-Extreme. We also obtain 98.93% solve rate on Maze-Unique. Surprisingly, progressive denoising is unnecessary at inference: hol
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzySimilar title/name (fuzzy) · 59%stabilityai/stable-diffusion-3.5-large →
“Fuzzy title match (0.73): “Diffusion as a Training Curriculum for Timestep-Free Iterati” ≈ “stabilityai/stable-diffusion-3.5-large””
- FuzzySimilar title/name (fuzzy) · 59%stabilityai/stable-diffusion-xl-base-1.0 →
“Fuzzy title match (0.73): “Diffusion as a Training Curriculum for Timestep-Free Iterati” ≈ “stabilityai/stable-diffusion-xl-base-1.0””
- FuzzySimilar title/name (fuzzy) · 59%CompVis/stable-diffusion-v1-4 →
“Fuzzy title match (0.73): “Diffusion as a Training Curriculum for Timestep-Free Iterati” ≈ “CompVis/stable-diffusion-v1-4””
- PossiblePossibly related (embedding) · 52%AI models get convenient amnesia about source material as they grow, MIT boffins find →
- PossiblePossibly related (embedding) · 48%Understand diffusion in 20 minutes →
- LinkedLinked via arxiv author · 85%Mariia Drozdova →
“Diffusion as a Training Curriculum for Timestep-Free Iterative Reasoning”
- LinkedLinked via arxiv author · 85%Aidan Sirbu →
“Diffusion as a Training Curriculum for Timestep-Free Iterative Reasoning”
- LinkedLinked via arxiv author · 85%Pietro Miotti →
“Diffusion as a Training Curriculum for Timestep-Free Iterative Reasoning”