The Limits of Speculation: Bounding Speculative Decoding in Mixture-of-Experts

Abstract:Speculative decoding in Mixture-of-Experts (MoE) models faces the problem of unstable verification cost caused by input-dependent expert loading. To study the physics of this process, we formulate speculation-budget selection as an offline Stochastic Shortest Path (SSP) problem over reference sequences and build a diagnostic Oracle that uses counterfactual simulation to account for MoE verification cost. A detailed analysis of the Oracle's decisions on the Qwen3-Coder and EAGLE-3 pairing, in the space of marginal deltas (Delta Space), shows that rejected candidates form a strict linear boundary. This result demonstrates that a complex global optimization is locally governed by a necessary condition balancing marginal cost against expected progress ($\frac{\Delta \mathbb{E}[Cost]}{\Delta \mathbb{E}[a]}$), providing a rigorous mathematical reference point for designing future adaptive online heuristics.

Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2609.22156 [cs.LG]

(or arXiv:2609.22156v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.22156 Focus to learn more arXiv-issued DOI via DataCite

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