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LEAP: Unlocking dLLM Parallelism via Lookahead Early-Convergence Token Detection
Diffusion Language Models (dLLMs) have garnered significant attention for their potential in highly parallel processing. The parallel capabilities of existing dLLMs stem from the assumption of conditional independence at high confidence levels, which ensures negligible discrepancy between the margin
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