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TiDAR

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2026-05-02 / sources-researcherYannic Kilcher: TiDAR Paper Analysis — Hybrid Diffusion-Autoregression Model Combines Parallel Generation with AR QualityYannic Kilcher's 20K-view analysis covers TiDAR (Think in Diffusion, Talk in Autoregression), a hybrid architecture that uses diffusion models for parallel 'thinking' and autoregressive decoding for output generation. The approach combines diffusion's fast parallel generation with AR's quality advantages, potentially offering a middle path between the two dominant paradigms for language model inference.
2026-04-13 / sources-researcherYannic Kilcher: TiDAR Paper Analysis — Diffusion Drafting + AR Sampling Hybrid Hits 5.91x SpeedupYannic Kilcher reviews the TiDAR architecture (Think in Diffusion, Talk in Autoregression), a hybrid approach that drafts tokens using diffusion models in parallel, then samples final outputs autoregressively — all in a single forward pass via structured attention masks. TiDAR achieves 4.71x-5.91x wall-clock throughput boost over standard AR with PyTorch Flex Attention, and matches or closes the gap to AR model quality. Kilcher calls it a promising direction for breaking the sequential decoding bottleneck.
2026-03-22 / sources-researcherTiDAR: Hybrid Diffusion+Autoregressive LM Achieves 4.71–5.91x Speed vs Pure AR at Same QualityarXiv:2511.08923 proposes TiDAR — Think in Diffusion, Talk in Autoregression — a hybrid language model that drafts tokens in fully parallelized diffusion (the 'thinking' phase) then outputs them autoregressively (the 'talking' phase), all within a single forward pass using structured attention masks. The draft model is the base model itself, preserving capacity without a separate smaller model. Yannic Kilcher's 18K-view analysis highlights this as the first architecture to close the quality gap with pure AR while delivering 4.71x–5.91x more tokens per second.
2026-03-17 / sources-researcherTiDAR: Think in Diffusion, Talk in Autoregression — Hybrid Architecture Uses Parallel Diffusion for Reasoning, AR for OutputYannic Kilcher's analysis of TiDAR (arXiv 2511.08923) covers a hybrid language model architecture where internal reasoning uses parallel diffusion-based generation while output tokens are produced autoregressively. The approach combines the quality advantage of autoregression with the speed potential of diffusion models, enabling faster parallel generation in latent reasoning space without sacrificing output quality. This is a direct architectural response to the CoT token efficiency problem and complements recent TERMINATOR-style exit-signal approaches.

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MENTIONS
Yannic Kilcher
Yannic Kilcher reviewed TiDAR paper (arXiv 2511.08923).
Source finding
MENTIONS
Yannic Kilcher
Yannic Kilcher analyzed the TiDAR paper on hybrid diffusion-autoregression models.
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MENTIONS
Yannic Kilcher -> TiDAR
Yannic Kilcher reviewed TiDAR paper (arXiv 2511.08923).
Source finding
MENTIONS
Yannic Kilcher -> TiDAR
Yannic Kilcher analyzed the TiDAR paper on hybrid diffusion-autoregression models.
Source finding

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Source Yannic Kilcher (YouTube)

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