Top 100 AI · France
Maxime Labonne
Liquid AI 
- Score
- 7,473+14 today
- Rank
- 61/198 ↓6
- over 90 days
- +157+2.0%
Maxime Labonne is a Machine Learning Scientist specializing in Large Language Models (LLMs) and Graph Neural Networks. He holds a PhD from the Polytechnic Institute of Paris and is recognized as an AI/ML Google Developer Expert. Labonne has developed several popular LLMs, including AlpahMonarch-7B, Beyonder-4x7B, Phixtral, and NeuralBeagle14, and has created tools like LLM AutoEval and LazyMergekit.
Data as of Oct 25, 2026.
The raw numbers · last 90 days
- Median views
- 39.7K
- per post
- Median engagements
- 496
- per post
- Posts
- 96
- over 90 days
- Followers
- 120.4K
- all scored platforms
What drives the score
Percentiles 0–100 against all tracked creators.
- Reach · 30%85
Average views per post over 90 days
- Engagement · 30%72
Average reactions per post
- Audience · 30%74
Total followers, relative to population
- Consistency · 10%100
Posting frequency (25 posts / 90 days = 100)
Platforms
120.4K total followers
| Platform | Followers | Score |
|---|---|---|
| X@maximelabonne | 45.1K | 8,010t2 |
| LinkedIn@maxime-labonne | 75.3K | 11,190t1 |
Last 30 days
Labonne's top posts
LinkedIn · 28 days ago#1MLGoodbye 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 views1.1K engagements
LinkedIn · 7 days ago#2MLCan 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 views671 engagements
LinkedIn · 13 days ago#3MLWe 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 views661 engagements
- X · 24 days ago#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…
ML41.9K views620 engagements
X · 28 days ago#5MLLife update: goodbye London, I moved to SF (still with @liquidai) 🫡 Let's grab coffee? https://t.co/bVgoyT9zJR
16.6K views481 engagements
LinkedIn · 3 days ago#6MLUnderstanding 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 views389 engagements
Head-to-head
