AI BriefingBlockPilot: Adaptive Block Size Prediction to Speed Up Inference
Key point
A technique is proposed that maximizes the efficiency of Diffusion-based Speculative Decoding by predicting the optimal block size according to the input data.
Details
Existing Diffusion-based Speculative Decoding methods had the limitation of applying the same strategy to all inputs by using a fixed block size. However, based on the observation that the optimal block size differs for each input sample, we propose a new method called BlockPilot.
The key features of BlockPilot are as follows:
- Instance-Adaptive Policy: Uses a lightweight policy-learning model that predicts the optimal block size based on the representation from the Prefilling stage.
- Low Overhead: Since the prediction is performed only once, right after Prefilling, the overhead on the overall inference process is very small.
- Plug-and-Play: Has a structure that can be easily integrated into existing systems.
Experimental results demonstrated excellent efficiency, recording a 4.20x speedup and an acceptance length of 5.92 on the Qwen3-4B model (at Temperature T=1).
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