arXiv: cs.AI preprintsInternational5 November 2026
Split-KV: Efficient Context Parallelism for Training Diffusion Drafters
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arXiv:2611.05926v1 Announce Type: new Abstract: Diffusion drafters accelerate speculative decoding by denoising multiple draft tokens in parallel. Their training uses anchor sampling to construct compact draft queries and performs cross-attention with a block-sparse mask over a much longer context. We empirically show that acceptance length remains stable as context scales with a fixed query budget, while increasing the query budget yields diminishing quality gains, jointly establishing a practical regime with queries far shorter than the context. No existing context parallelism (CP) method jo
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