Author

Acyr F. Locatelli

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#artificial intelligence Preprint Oct 2026

Why Speculative Decoding Works Better Than Predicted on Sparse MoE Models

Speculative decoding (SD) accelerates autoregressive inference by leveraging the memory bound nature of decoding, verifying multiple draft tokens in a single forward pass. For mixture-of-experts (MoE) models, prior work predicts a concave speedup-batch size relationship, with gains peaking at intermediate batch sizes d...

Ekagra Ranjan, Komal K. Teru, Bharat Venkitesh et al. · 0 citations

#machine learning Preprint Sep 2026

Legibility is Not Interpretability: Comparing Judged and Actual Importance in Chain-Of-Thought Reasoning

This work operationalizes the importance of a reasoning step as its advantage: the change in expected reward, e.g., producing the correct final answer, from including that step, estimated via Monte Carlo rollouts, and evaluates whether LLM judges can identify high-advantage steps and finds that sufficiently capable LLM...

Kevin Du, A. Hoyle, L. Ruis et al. · 1 citation