[2603.03251] Speculative Speculative Decoding

Computer Science > Machine Learning arXiv:2603.03251 (cs) [Submitted on 3 Mar 2026] Title:Speculative Speculative Decoding Authors:Tanishq Kumar, Tri Dao, Avner May View a PDF of the paper titled Speculative Speculative Decoding, by Tanishq Kumar and 2 other authors View PDF HTML (experimental) Abstract:Autoregressive decoding is bottlenecked by its sequential nature. Speculative decoding has become a standard way to accelerate inference by using a fast draft model to predict upcoming tokens from a slower target model, and then verifying them in parallel with a single target model forward pass. However, speculative decoding itself relies on a sequential dependence between speculation and verification. We introduce speculative speculative decoding (SSD) to parallelize these operations. While a verification is ongoing, the draft model predicts likely verification outcomes and prepares speculations pre-emptively for them. If the actual verification outcome is then in the predicted set, a speculation can be returned immediately, eliminating drafting overhead entirely. We identify three key challenges presented by speculative speculative decoding, and suggest principled methods to solve each. The result is Saguaro, an optimized SSD algorithm. Our implementation is up to 2x faster than optimized speculative decoding baselines and up to 5x faster than autoregressive decoding with open source inference engines. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2603.03251 [cs.LG]  ...