PEARL: Parallel Speculative Decoding with Adaptive Draft Length
Tianyu Liu Yun Li Qitan Lv Kai Liu Jianchen Zhu Winston Hu Xiao Sun
Speculative decoding (SD), where an extra draft model is employed to provide multiple **draft** tokens first and then the original target model verifies these tokens in parallel, has shown great power for LLM inference acceleration.
However, existing SD methods suffer from the mutual waiting problem, i.e., the target model gets stuck when the draft model is *guessing* tokens, and vice versa. This problem is directly incurred by the asynchronous execution of the draft model and the target model, and is exacerbated due to the fixed draft length in speculative decoding.
To address these challenges, we propose a conceptually simple, flexible, and general framework to boost speculative decoding, namely 
**P**arallel sp**E**culative decoding with **A**daptive d**R**aft **L**ength (PEARL). 
Specifically, PEARL proposes *pre-verify* to verify the first draft token in advance during the drafting phase, and *post-verify* to generate more draft tokens during the verification phase.
PEARL parallels the drafting phase and the verification phase via applying the two strategies, and achieves adaptive draft length for different scenarios, which effectively alleviates the mutual waiting problem.
Experiments on various text generation benchmarks demonstrate the effectiveness of our PEARL, leading to a superior speedup performance up to **4.43$\times$** and **1.50$\times$**, compared to auto-regressive decoding and vanilla speculative decoding, respectively.
speculative decoding, inference acceleration, large language models
we introduce PEARL (Parallel spEculative decoding with Adaptive dRaft Length) to further reduce the inference latency of Large Language Models (LLMs).