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MLMaxime Labonne, Head of post-training, Liquid AI

Top 100 KI · Frankreich

Maxime Labonne

Liquid AI Frankreich

Score
7.4590 heute
Rang
64/198 ↓9
in 90 Tagen
+143+1,8 %

Maxime Labonne ist ein Machine Learning Scientist, der sich auf Large Language Models (LLMs) und Graph Neural Networks spezialisiert hat. Er hat einen PhD vom Polytechnischen Institut in Paris und ist als AI/ML Google Developer Expert anerkannt. Labonne hat mehrere populäre LLMs entwickelt, darunter AlpahMonarch-7B, Beyonder-4x7B, Phixtral und NeuralBeagle14, sowie Tools wie LLM AutoEval und LazyMergekit kreiert.

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Stand: 25. Okt. 2026.

Die Rohdaten · letzte 90 Tage

Median-Aufrufe
38.864
pro Beitrag
Median-Interaktionen
490
pro Beitrag
Beiträge
97
in 90 Tagen
Follower
119.965
alle gewerteten Netzwerke

Woraus sich der Score ergibt

Perzentile 0 – 100 im Vergleich zu allen erfassten Creators.

  • Reichweite · 30 %85

    Ø Aufrufe pro Beitrag über 90 Tage

  • Engagement · 30 %72

    Ø Reaktionen pro Beitrag

  • Publikum · 30 %74

    Follower gesamt, im Verhältnis zur Bevölkerung

  • Regelmäßigkeit · 10 %100

    Posting-Rhythmus (25 Posts / 90 T = 100)

Netzwerke

119.965 Follower insgesamt

NetzwerkFollowerScore
X@maximelabonne44.7228.004t2
LinkedIn@maxime-labonne75.24311.182t1

Letzte 30 Tage

Die besten Posts von 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 · vor 28 Tagen#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…

    80.775 Aufrufe1077 Interaktionen

  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 · vor 7 Tagen#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…

    53.475 Aufrufe668 Interaktionen

  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 · vor 13 Tagen#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…

    53.075 Aufrufe661 Interaktionen

  4. X · vor 24 Tagen#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

    41.905 Aufrufe620 Interaktionen

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

    16.621 Aufrufe481 Interaktionen

  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 · vor 3 Tagen#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…

    30.575 Aufrufe366 Interaktionen

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