---
title: "Rémi Munos — author profile and papers — Elamur Research"
description: "Rémi Munos, École Normale Supérieure de Lyon: 3 papers on Elamur Research (2023–2025), h-index 66 — affiliations, topics, co-authors…"
canonical: "https://research.elamur.ai/free/en/authors/remi-munos/"
---
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# Rémi Munos

Rémi Munos has 3 papers in our library, in 2023–2025, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/); often with [Daniele Calandriello](https://md.research.elamur.ai/free/en/authors/daniele-calandriello/), [Mark Rowland](https://md.research.elamur.ai/free/en/authors/mark-rowland/), [Michal Valko](https://md.research.elamur.ai/free/en/authors/michal-valko/).

3 papers in our library · 2023–2025

[OpenAlex](https://openalex.org/A5006533777) · [**h-index 66**](https://openalex.org/authors/A5006533777) · [465 works](https://openalex.org/works?filter=author.id:A5006533777) · [19,465 citations](https://openalex.org/works?filter=author.id:A5006533777&sort=cited_by_count:desc) OpenAlex, 2026-10-14

[📄 3 works in our library](https://md.research.elamur.ai/free/en/authors/remi-munos/#papers) · [🔗 Cited 990 times in our library](https://md.research.elamur.ai/free/en/authors/remi-munos/cited-by/) · [12 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #5639](https://md.research.elamur.ai/free/en/paper-stars/page/57/#star-5639)

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=R%C3%A9mi+Munos) · [Semantic Scholar](https://www.semanticscholar.org/search?q=R%C3%A9mi+Munos) · [dblp](https://dblp.org/search?q=R%C3%A9mi+Munos) · [ACL Anthology](https://aclanthology.org/search/?q=R%C3%A9mi+Munos)

## Affiliations

[🔬**Institut national de recherche en sciences et technologies du numérique** Le Chesnay-Rocquencourt · France 2007–2026 · 64 papers*current*](https://md.research.elamur.ai/free/en/labs/institut-national-de-recherche-en-sciences-et-technologies-du-numerique/) · [🔬**Centre Inria de l'Université de Lille** Villeneuve-d'Ascq · France 2008–2026 · 60 papers*current*](https://md.research.elamur.ai/free/en/labs/centre-inria-de-l-universite-de-lille/) · [🏢**Google DeepMind** London · United Kingdom 2015–2026 · 52 papers*current*](https://md.research.elamur.ai/free/en/companies/google-deepmind/) · [🏢**Microsoft Research New England** Cambridge · United States 2014–2026 · 3 papers*current*](https://ror.org/010wkqn50) · [🏢**Microsoft Research** Cambridge · United Kingdom 2026 · 2 papers*current*](https://md.research.elamur.ai/free/en/companies/microsoft-research-united-kingdom/) · [🎓**École Normale Supérieure de Lyon** Lyon · France 2026 · 2 papers*current*](https://md.research.elamur.ai/free/en/universities/ecole-normale-superieure-de-lyon/) · [🎓**Courant Institute of Mathematical Sciences** New York · United States 2026 · 1 paper*current*](https://md.research.elamur.ai/free/en/universities/courant-institute-of-mathematical-sciences/) · [🎓**University of Cambridge** Cambridge · United Kingdom 2026 · 1 paper*current*](https://md.research.elamur.ai/free/en/universities/university-of-cambridge/) · [🏢**Google** Mountain View · United States 2016–2023 · 32 papers](https://md.research.elamur.ai/free/en/companies/google/) · [🎓**Université de Lille** Lille · France 2007–2015 · 10 papers](https://md.research.elamur.ai/free/en/universities/universite-de-lille/)

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 14 more institutions on single bylines are not listed.

## Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 6 The field of science this work belongs to (OpenAlex fields).[Artificial Intelligence · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Computer Science · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Decision Sciences · 2 works](https://md.research.elamur.ai/free/en/shelves/decision-sciences/) · [Management Science and Operations Research · 2 works](https://md.research.elamur.ai/free/en/shelves/decision-sciences/management-science-and-operations-research/) · [Arts and Humanities · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Language and Linguistics · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/)

🧭 Topics 2 Research subjects and languages the work is about.[Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/) · [Reasoning · 1 work](https://md.research.elamur.ai/free/en/topics/reasoning/)

## Works by year

### 2020s

[**2025** 1 paper](https://md.research.elamur.ai/free/en/authors/remi-munos/year/2025/) · [**2023** 2 papers](https://md.research.elamur.ai/free/en/authors/remi-munos/year/2023/)

## Papers

The most cited first (OpenAlex and our library).

1. **[A General Theoretical Paradigm to Understand Learning from Human Preferences](https://md.research.elamur.ai/free/en/papers/azar-2023-general-theoretical-paradigm-understand-learning/)** *(2023 · 263 citations)* This paper by researchers at Google DeepMind provides a theoretical foundation for methods that train language models from human preferences.
2. **[Nash Learning from Human Feedback](https://md.research.elamur.ai/free/en/papers/munos-2023-nash-learning-human-feedback/)** *(2023 · 32 citations)* Standard RLHF reduces pairwise human judgments to a scalar reward, typically through a Bradley-Terry model, and then optimizes the LLM against that score.
3. **[Outcome-based Exploration for LLM Reasoning](https://md.research.elamur.ai/free/en/papers/song-2025-outcome-based-exploration-llm-reasoning/)** *(2025 · 15 citations)* Reinforcement learning that rewards only a correct final answer improves LLM math reasoning, but it also narrows the range of answers a model produces.

## All works

15 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

1. **[Bootstrap your own latent: A new approach to self-supervised Learning ↗](https://arxiv.org/abs/2006.07733) arXiv (Cornell University) · no summary yet**Jean-Bastien Grill et al.*2020 · 3,531 citations*
2. **[IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures ↗](https://arxiv.org/abs/1802.01561) arXiv:1802.01561 · no summary yet**Lasse Espeholt et al.*2018 · 710 citations*
3. **[Noisy Networks for Exploration ↗](https://arxiv.org/abs/1706.10295) arXiv (Cornell University) · no summary yet**Meire Fortunato et al.*2017 · 570 citations*
4. **[Unifying Count-Based Exploration and Intrinsic Motivation ↗](https://arxiv.org/abs/1606.01868) arXiv:1606.01868 · no summary yet**Marc G. Bellemare et al.*2016 · 311 citations*
5. [**A General Theoretical Paradigm to Understand Learning from Human Preferences arXiv:2310.12036**Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot, et al.*2023 · 263 citations*](https://md.research.elamur.ai/free/en/papers/azar-2023-general-theoretical-paradigm-understand-learning/)
6. **[A Study on Overfitting in Deep Reinforcement Learning ↗](https://arxiv.org/abs/1804.06893) arXiv (Cornell University) · no summary yet**Chiyuan Zhang et al.*2018 · 242 citations*
7. **[Sample Efficient Actor-Critic with Experience Replay ↗](https://arxiv.org/abs/1611.01224) arXiv (Cornell University) · no summary yet**Ziyu Wang et al.*2016 · 220 citations*
8. **[Automated Curriculum Learning for Neural Networks ↗](https://arxiv.org/abs/1704.03003) arXiv:1704.03003 · no summary yet**Alex Graves et al.*2017 · 111 citations*
9. **[Combining policy gradient and Q-learning ↗](https://arxiv.org/abs/1611.01626) arXiv (Cornell University) · no summary yet**Brendan O’Donoghue et al.*2016 · 98 citations*
10. **[Memory-Efficient Backpropagation Through Time ↗](https://arxiv.org/abs/1606.03401) arXiv (Cornell University) · no summary yet**Audrūnas Gruslys et al.*2016 · 91 citations*
11. **[Finite time bounds for sampling based fitted value iteration ↗](https://doi.org/10.1145/1102351.1102462) International Conference on Machine Learning · no summary yet**Csaba Szepesvári, Rémi Munos*2005 · 60 citations*
12. [**Nash Learning from Human Feedback arXiv:2312.00886**Remi Munos, Michal Valko, Daniele Calandriello, et al.*2023 · 32 citations*](https://md.research.elamur.ai/free/en/papers/munos-2023-nash-learning-human-feedback/)
13. **[Learning to Search with MCTSnets ↗](https://arxiv.org/abs/1802.04697) arXiv:1802.04697 · no summary yet**Arthur Guez et al.*2018 · 30 citations*
14. [**Outcome-based Exploration for LLM Reasoning arXiv:2509.06941**Yuda Song, Julia Kempe, Remi Munos*2025 · 15 citations*](https://md.research.elamur.ai/free/en/papers/song-2025-outcome-based-exploration-llm-reasoning/)
15. **[GitSwarm: Decentralized Compounding Inference ↗](https://arxiv.org/abs/2610.04862) arXiv (Cornell University) · no summary yet**Vedant Shah et al.*2026*

## Co-authors here

10 co-authors with a page, the most papers together first.

- [Daniele Calandriello](https://md.research.elamur.ai/free/en/authors/daniele-calandriello/) ×2
- [Mark Rowland](https://md.research.elamur.ai/free/en/authors/mark-rowland/) ×2
- [Michal Valko](https://md.research.elamur.ai/free/en/authors/michal-valko/) ×2
- [Mohammad Gheshlaghi Azar](https://md.research.elamur.ai/free/en/authors/mohammad-gheshlaghi-azar/) ×2
- [Andrea Michi](https://md.research.elamur.ai/free/en/authors/andrea-michi/)
- [Bilal Piot](https://md.research.elamur.ai/free/en/authors/bilal-piot/)
- [Daniel Guo](https://md.research.elamur.ai/free/en/authors/daniel-guo/)
- [Julia Kempe](https://md.research.elamur.ai/free/en/authors/julia-kempe/)
- [Matthieu Geist](https://md.research.elamur.ai/free/en/authors/matthieu-geist/)
- [Sertan Girgin](https://md.research.elamur.ai/free/en/authors/sertan-girgin/)

## Venues

Where their works here were published, the most works first, with the editions of those that have a page.

- International Conference on Machine Learning

## Cited by

Papers of our library cite their works 990 times (of the 149,439 papers whose bibliographies we read); [138 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/remi-munos/cited-by/).

Updated 3 November 2026