DFlash: Parallel Speculative Decoding via Lightweight Block Diffusion
cneuralnetwork · x · 2026-08-03
An article explaining the DFlash paper, which introduces a parallel speculative decoding framework using a lightweight block diffusion model.
- Pain point: Current speculative decoding methods (like EAGLE-3) still rely on autoregressive drafting, where sequential generation limits further speedup.
- Core idea: Uses a small diffusion drafter conditioned on the hidden features of the target model to predict future token blocks in parallel, rather than reasoning from scratch.
- Advantage: Compared to massive pure diffusion drafters (around 7B params), this lightweight approach significantly boosts serving speed while maintaining high draft quality and acceptance rates.
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