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MLMaxime Labonne, Head of post-training, Liquid AI

Top 100 IA · Francia

Maxime Labonne

Liquid AI Francia

Punteggio
7473+14 oggi
Posizione
61/198 ↓6
in 90 giorni
+157+2,0%

Maxime Labonne est un scientifique en Machine Learning spécialisé dans les modèles de langage de grande taille (LLM) et les réseaux de neurones graphiques. Titulaire d'un doctorat de l'Institut Polytechnique de Paris, il est reconnu comme Expert Développeur Google en IA/ML. Labonne a développé plusieurs LLM populaires, tels qu'AlpahMonarch-7B, Beyonder-4x7B, Phixtral et NeuralBeagle14, et a créé des outils comme LLM AutoEval et LazyMergekit.

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Dati al 25 ott 2026.

I numeri grezzi · ultimi 90 giorni

Visualizzazioni mediane
39,7K
per post
Interazioni mediane
496
per post
Post
96
in 90 giorni
Follower
120,4K
tutti i social nel punteggio

Cosa determina il punteggio

Percentili 0 – 100 rispetto a tutti i creator monitorati.

  • Portata · 30%85

    Visualizzazioni medie per post su 90 giorni

  • Engagement · 30%72

    Reazioni medie per post

  • Audience · 30%74

    Follower totali, rapportati alla popolazione

  • Costanza · 10%100

    Ritmo di pubblicazione (25 post / 90 g = 100)

Social

120,4K follower in totale

SocialFollowerPunteggio
X@maximelabonne45,1K8010t2
LinkedIn@maxime-labonne75,3K11.190t1

Ultimi 30 giorni

I migliori post di Labonne

  1. Goodbye London, hello San Francisco Life update: I moved to SF (with Liquid AI). It's quite nice to have an office after being fully remote for over two years. So far, the city is really pretty and the weather is actu…
    LinkedIn · 29 gg fa#1
    MLMaxime Labonne, Head of post-training, Liquid AIMaxime Labonne

    Goodbye London, hello San Francisco Life update: I moved to SF (with Liquid AI). It's quite nice to have an office after being fully remote for over two years. So far, the city is really pretty and the weather is actu…

    80,8K visualizzazioni1,1K interazioni

  2. Can you train an LLM to say “I don’t know” without it saying it all the time? With reinforcement learning with verifiable rewards (RLVR), a model is rewarded for answering correctly. Saying "I don't know" is typically…
    LinkedIn · 8 gg fa#2
    MLMaxime Labonne, Head of post-training, Liquid AIMaxime Labonne

    Can you train an LLM to say “I don’t know” without it saying it all the time? With reinforcement learning with verifiable rewards (RLVR), a model is rewarded for answering correctly. Saying "I don't know" is typically…

    53,7K visualizzazioni671 interazioni

  3. We just released LFM2.5-24B-A2B! 🥳 It's an extremely fast agentic model, competitive with models with 50% more parameters. We expanded the context window to 131k and training up to 36T tokens. On the post-training si…
    LinkedIn · 14 gg fa#3
    MLMaxime Labonne, Head of post-training, Liquid AIMaxime Labonne

    We just released LFM2.5-24B-A2B! 🥳 It's an extremely fast agentic model, competitive with models with 50% more parameters. We expanded the context window to 131k and training up to 36T tokens. On the post-training si…

    53,1K visualizzazioni661 interazioni

  4. X · 24 gg fa#4
    Great read: SFT generalizes worse than RL because the data is off-policy, not because of the SFT objective. Rewriting the trajectories from an expert to look like what the base model writes can match or beat on-policy…
    MLMaxime Labonne, Head of post-training, Liquid AIMaxime Labonne

    41,9K visualizzazioni620 interazioni

  5. Life update: goodbye London, I moved to SF (still with @liquidai) 🫡 Let's grab coffee? https://t.co/bVgoyT9zJR
    X · 29 gg fa#5
    MLMaxime Labonne, Head of post-training, Liquid AIMaxime Labonne

    Life update: goodbye London, I moved to SF (still with @liquidai) 🫡 Let's grab coffee? https://t.co/bVgoyT9zJR

    16,6K visualizzazioni481 interazioni

  6. Understanding LLM Parameter Update Sparsity through the Lens of Fisher It's a cool paper that offers a competing explanation to the traditional "RL's updates are low rank because you get about 1 bit of information per…
    LinkedIn · 4 gg fa#6
    MLMaxime Labonne, Head of post-training, Liquid AIMaxime Labonne

    Understanding LLM Parameter Update Sparsity through the Lens of Fisher It's a cool paper that offers a competing explanation to the traditional "RL's updates are low rank because you get about 1 bit of information per…

    32,6K visualizzazioni389 interazioni

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