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MLMaxime Labonne, Head of post-training, Liquid AI

Top 100 IA · França

Maxime Labonne

Liquid AI França

Score
7.4590 hoje
Posição
64/198 ↓9
em 90 dias
+143+1,8%

Maxime Labonne é um Cientista de Machine Learning especializado em Large Language Models (LLMs) e Graph Neural Networks. Ele possui um PhD pelo Instituto Politécnico de Paris e é reconhecido como Google Developer Expert em AI/ML. Labonne desenvolveu diversos LLMs populares, como AlpahMonarch-7B, Beyonder-4x7B, Phixtral e NeuralBeagle14, além de criar ferramentas como LLM AutoEval e LazyMergekit.

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Dados de 25 de out. de 2026.

Os números brutos · últimos 90 dias

Visualizações medianas
38,9 mil
por publicação
Interações medianas
490
por publicação
Publicações
97
em 90 dias
Seguidores
120 mil
todas as redes pontuadas

O que compõe o score

Percentis 0 – 100 em relação a todos os criadores acompanhados.

  • Alcance · 30%85

    Média de visualizações por publicação em 90 dias

  • Engajamento · 30%72

    Média de reações por publicação

  • Audiência · 30%74

    Seguidores somados, em relação à população

  • Regularidade · 10%100

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

Redes

120 mil seguidores somados

RedeSeguidoresScore
X@maximelabonne44,7 mil8.004t2
LinkedIn@maxime-labonne75,2 mil11.182t1

Últimos 30 dias

Os melhores posts 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 · há 28 dias#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 visualizações1,1 mil interações

  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 · há 7 dias#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 visualizações668 interações

  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 · há 13 dias#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 visualizações661 interações

  4. X · há 24 dias#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 visualizações620 interações

  5. Life update: goodbye London, I moved to SF (still with @liquidai) 🫡 Let's grab coffee? https://t.co/bVgoyT9zJR
    X · há 28 dias#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 visualizações481 interações

  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 · há 3 dias#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 visualizações366 interações

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