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  "text": "(1/n) Tiny-A2D: An Open Recipe to Turn Any AR LM into a Diffusion LM\n\nCode (dLLM): https://t.co/Nv7d1t8Qin\nCheckpoints: https://t.co/rpibkb2Xfq\n\nWith dLLM, you can turn ANY autoregressive LM into a diffusion LM (parallel generation + infilling) with minimal compute. Using this recipe, we built a 🤗collection of the smallest diffusion LMs that work well in practice.\n\nKey takeaways:\n\n1. Finetuned on Qwen3-0.6B, we obtain the strongest small (~0.5/0.6B) diffusion LMs to date.\n\n2. The base AR LM matters: Investing compute in improving the base AR model is potentially more efficient than scaling compute during adaptation.\n\n3. Block diffusion (BD3LM) generally outperforms vanilla masked diffusion (MDLM), especially on math-reasoning and coding tasks.",
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