P-EAGLE: Parallel-Drafting EAGLE with Scalable Training

Reasoning LLMs produce longer outputs, requiring speculative decoding drafters trained on extended sequences. Parallel drafting—predicting multiple tokens per forward pass—offers latency benefits over sequential generation, but training complexity scales quadratically with the product of sequence length and parallel positions, rendering long-context training impractical. We present P(arallel)-EAGLE, which transforms EAGLE from autoregressive to parallel multi-token prediction via a learnable shared hidden state. To scale training to long contexts, we develop a framework featuring attention mask pre-computation and sequence partitioning techniques, enabling gradient accumulation \textit{within} individual sequences for parallel-prediction training. We implement P-EAGLE in vLLM and demonstrate speedups of 1.10×–1.36× over autoregressive EAGLE-3 across GPT-OSS 120B, 20B, and Qwen3-Coder 30B.