# Mentored Decoding: Faster Inference meets Boosting

arXiv: https://arxiv.org/abs/2609.30474 · HTML: https://mlsanity.com/paper/2609.30474/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Speculative decoding, one of 12 directions in Efficient LLM inference and serving.
- Builds on 7 of its 54 references that are in the map.
- First posted on arXiv 24 Sep 2026.

## Placed in

- Efficient LLM inference and serving / [Speculative decoding](https://mlsanity.com/inference/speculative-decoding/)

## Builds on (most cited first, 7 of 7)

- [Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads](https://mlsanity.com/paper/2401.10774/) (2024)
- [Better & Faster Large Language Models via Multi-token Prediction](https://mlsanity.com/paper/2404.19737/) (2024)
- [Break the Sequential Dependency of LLM Inference Using Lookahead Decoding](https://mlsanity.com/paper/2402.02057/) (2024)
- [DFlash: Block Diffusion for Flash Speculative Decoding](https://mlsanity.com/paper/2602.06036/) (2026)
- [Inference with Reference: Lossless Acceleration of Large Language Models](https://mlsanity.com/paper/2304.04487/) (2023)
- [A Practical Investigation of Training-free Relaxed Speculative Decoding](https://mlsanity.com/paper/2607.08690/) (2026)
- [Beyond the Target: From Imitation to Collaboration in Speculative Decoding](https://mlsanity.com/paper/2605.24793/) (2026)

## Abstract (arXiv)

Speculative decoding is a successful technique speeding up inference of a target autoregressive language model via a fast drafter model. Lossy speculative decoding allows a drift with respect to the target to further improve speed. Interestingly, it has been observed experimentally that the resulting model can textitalso beat the target textitquality-wise. Our paper formally proves how such a feat is possible with a formal approach to lossy speculative decoding called textitmentored decoding. To get there, we connect inference to a celebrated ML training theory, textitboosting, and proceed via the generalization of mentored decoding to the whole set of f-divergences. We uncover key properties of mentored decoding, among which (i) the particularly appealing geometric nature of the total variation case, (ii) simple approximations for any f-divergence in direct relation with boosting compliance, and (iii) a textitdivergence independent O(n) space and O(sort(n)) time data structure built on drafter and target outputs, which allows to query the optimal parameters of the dual problem in O(log n) time and constructing optimal mentored distributions in O(n) time for any f-divergence.