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Reading: Redefining Induction: Multi-Token vs. Next-Token on High-Quality LLM Data | HackerNoon
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World of Software > Computing > Redefining Induction: Multi-Token vs. Next-Token on High-Quality LLM Data | HackerNoon
Computing

Redefining Induction: Multi-Token vs. Next-Token on High-Quality LLM Data | HackerNoon

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Last updated: 2025/07/23 at 2:56 PM
News Room Published 23 July 2025
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Table of Links

Abstract and 1. Introduction

2. Method

3. Experiments on real data

4. Ablations on synthetic data

5. Why does it work? Some speculation

6. Related work

7. Conclusion, Impact statement, Environmental impact, Acknowledgements and References

A. Additional results on self-speculative decoding

B. Alternative architectures

C. Training speeds

D. Finetuning

E. Additional results on model scaling behavior

F. Details on CodeContests finetuning

G. Additional results on natural language benchmarks

H. Additional results on abstractive text summarization

I. Additional results on mathematical reasoning in natural language

J. Additional results on induction learning

K. Additional results on algorithmic reasoning

L. Additional intuitions on multi-token prediction

M. Training hyperparameters

J. Additional results on induction learning

:::info
Authors:

(1) Fabian Gloeckle, FAIR at Meta, CERMICS Ecole des Ponts ParisTech and Equal contribution;

(2) Badr Youbi Idrissi, FAIR at Meta, LISN Université Paris-Saclayand and Equal contribution;

(3) Baptiste Rozière, FAIR at Meta;

(4) David Lopez-Paz, FAIR at Meta and a last author;

(5) Gabriel Synnaeve, FAIR at Meta and a last author.

:::


:::info
This paper is available on arxiv under CC BY 4.0 DEED license.

:::

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