FastEagle: Cascaded Drafting for Accelerating Speculative Decoding

• URL: http://arxiv.org/abs/2509.20416v1
• Date: Wed, 24 Sep 2025 09:38:32 GMT
• Title: FastEagle: Cascaded Drafting for Accelerating Speculative Decoding
• Authors: Haiduo Huang, Jiangcheng Song, Wenzhe Zhao, Pengju Ren,
• Abstract summary: We present FastEagle, a non-autoregressive cascaded drafter that emits an entire draft in a single forward pass.<n>FastEagle delivers substantial wall-clock speedups over strong autoregressive drafters while maintaining competitive acceptance behavior.
• Score: 6.482154864678126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speculative decoding accelerates generation by drafting candidates and verifying them in parallel, yet state-of-the-art drafters (e.g., EAGLE) still require N sequential passes to propose N tokens. We present FastEagle, a non-autoregressive cascaded drafter that emits an entire draft in a single forward pass. FastEagle replaces temporal steps with a lightweight layer cascade and trains with layer-wise supervision to mitigate error accumulation. Coupled with a constrained draft tree that preserves lossless verification cost, FastEagle delivers substantial wall-clock speedups over strong autoregressive drafters while maintaining competitive acceptance behavior. Across multiple LLMs (Vicuna-13B, LLaMA-Instruct 3.x, and DeepSeek-R1-Distill-LLaMA) and tasks (MT-Bench, HumanEval, GSM8K, CNN/DM, Alpaca), FastEagle consistently outperforms EAGLE-3 in speedup under both greedy and stochastic decoding, with comparable average acceptance lengths. These results indicate that removing sequential dependencies in drafting is a practical path toward lossless LLM inference acceleration.

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