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MLMaxime Labonne, Head of post-training, Liquid AI

Top 100 IA · Francia

Maxime Labonne

Liquid AI Francia

Puntuación
74590 hoy
Puesto
64/198 ↓9
en 90 días
+143+1,8 %

Maxime Labonne es un científico especializado en aprendizaje automático, con foco en modelos de lenguaje grandes (LLMs) y redes neuronales de grafos. Es doctor por el Instituto Politécnico de París y está reconocido como Google Developer Expert en IA/aprendizaje automático. Labonne ha desarrollado varios LLMs populares, como AlpahMonarch-7B, Beyonder-4x7B, Phixtral y NeuralBeagle14, además de crear herramientas como LLM AutoEval y LazyMergekit.

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Datos a 25 oct 2026.

Las cifras brutas · últimos 90 días

Visualizaciones medianas
38,9 mil
por publicación
Interacciones medianas
490
por publicación
Publicaciones
97
en 90 días
Seguidores
120 mil
todas las redes puntuadas

Qué compone la puntuación

Percentiles 0 – 100 frente a todos los creadores seguidos.

  • Alcance · 30 %85

    Visualizaciones medias por publicación en 90 días

  • Interacción · 30 %72

    Reacciones medias por publicación

  • Audiencia · 30 %74

    Seguidores acumulados, en relación con la población

  • Regularidad · 10 %100

    Ritmo de publicación (25 posts / 90 d = 100)

Redes

120 mil seguidores acumulados

RedSeguidoresPuntuación
X@maximelabonne44,7 mil8004t2
LinkedIn@maxime-labonne75,2 mil11.182t1

Últimos 30 días

Las mejores publicaciones de 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 · hace 28 d#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…

    80,8 mil visualizaciones1,1 mil interacciones

  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 · hace 7 d#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…

    53,5 mil visualizaciones668 interacciones

  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 · hace 13 d#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…

    53,1 mil visualizaciones661 interacciones

  4. X · hace 24 d#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

    41,9 mil visualizaciones620 interacciones

  5. Life update: goodbye London, I moved to SF (still with @liquidai) 🫡 Let's grab coffee? https://t.co/bVgoyT9zJR
    X · hace 28 d#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

    16,6 mil visualizaciones481 interacciones

  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 · hace 3 d#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…

    30,6 mil visualizaciones366 interacciones

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