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This work investigates whether hybrid backbones can become effective DLMs by adapting Qwen3.5 at 0.8B, 2B, 4B, and 9B scales, and finds that they can be efficient starting points for adaptation.
Optimizing both token placement and remasking decisions in MDLMs leads to groundbreaking improvements in reasoning and coding tasks.
