Machine Learning and Agents at @Nvidia, 6x Kaggle Grandmaster CPMP. Arc Prize winner. ML PhD. Ex ENS Ulm, ILOG CPLEX, IBM. Views are my own.

France
Joined March 2012
Ivan Sorokin and I are the official winners on the Arc Prize competition, with a significant lead over other teams. Thanks to @kaggle and @arcprize for hosting the competition. NVIDIA tech blog summarizing what we did: developer.nvidia.com/blog/nv… Our writeup: kaggle.com/competitions/arc-… Our code: github.com/1ytic/NVARC
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JFPuget πŸ‡«πŸ‡·πŸ‡ΊπŸ‡¦πŸ‡¨πŸ‡¦πŸ‡¬πŸ‡± retweeted
Every day we are fighting to prevent French stats from looking like this.
Every day we are fighting to prevent American streets from looking like this.
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Say you have no clue without saying it: "LLMs are just a series of matrix multiplications." When will people get that some non linearity is key?
Spectacular Nature piece on why AI is not conscious and probably will never be. Three arguments: 1) Intelligence is not consciousness. Intelligence is about solving problems. Consciousness is about having subjective experiences. AlphaFold can solve super difficult problems. By contrast, our brains performs complex operations entirely outside awareness. Problem-solving and consciousness are two separate things. 2. Biology is essential. Our brains are alive, down to every cell. Their activity is inseparable from the living tissue that produces it. The idea that consciousness can be reproduced simply by running the right computations on silicon components who do not fight for their life is a very far fetching and, I would add, almost religious, assumption. 3) The physical organization may matter more than the computation. According to integrated information theory, consciousness requires specific causal relationships between a system’s physical components. The computer architectures running today’s AI lack this organization. The comparison with AlphaFold is particularly revealing: Almost nobody things AlphaFold is conscious, despite its inner working is very similar to an LLM Why? Because it does not speak. LLMs do speak like us and therefore we fall into an illusion: Since they speak like us, they must be like us. But this inference is wrong. LLMs are just a series of matrix multiplications. They are not conscious, and they will never be. * Paper in the first reply
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JFPuget πŸ‡«πŸ‡·πŸ‡ΊπŸ‡¦πŸ‡¨πŸ‡¦πŸ‡¬πŸ‡± retweeted
EXCLUSIVE: ByteDance, Alibaba, Tencent place over $35 billion worth of Nvidia chip orders reut.rs/4zipmhf reut.rs/4zipmhf
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JFPuget πŸ‡«πŸ‡·πŸ‡ΊπŸ‡¦πŸ‡¨πŸ‡¦πŸ‡¬πŸ‡± retweeted
Another banger from uncle Jensen
Nvidia CEO Jensen Huang to Chancellor Merz: I've also heard you say that you want to find that balance between innovation and regulation. There's nothing to regulate if the industry doesn't thrive.
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JFPuget πŸ‡«πŸ‡·πŸ‡ΊπŸ‡¦πŸ‡¨πŸ‡¦πŸ‡¬πŸ‡± retweeted
Ouch.
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JFPuget πŸ‡«πŸ‡·πŸ‡ΊπŸ‡¦πŸ‡¨πŸ‡¦πŸ‡¬πŸ‡± retweeted
Si vous avez tΓ©lΓ©chargΓ© aujourd'hui la derniΓ¨re version de CLI d'OVHcloud, merci de la mettre Γ  jour Γ  nouveau trΓ¨s rapidement. Sinon effacer. Il s'agit de repo: github.com/ovh/ovhcloud-cli La version CLI de 14h47 a Γ©tΓ© compromise par un hacker. Nous l'avons dΓ©tectΓ© et bloquΓ© Γ  16h21. Merci au client qui nous a remontΓ© le souci. La version compromise est v0.11.0 v0.12.0 v0.13.0 v0.14.0 v0.15.0. La version v0.10.0 et la future 0.16.0 est clean. On vient de finit les premiΓ¨res vΓ©rifications et aucun autre repo n'est affectΓ©. Nous avons aussi retracΓ© l'incident. Le hacker a utilisΓ© le compte github d'un des dΓ©veloppeurs et a ajoutΓ© un binaire dont le job est rΓ©cupΓ©rer les secrets sur le disque. La qualitΓ© du binaire est mΓ©diocre mais il peut aller jusqu'au bout. Si vous Γͺtes concernΓ©s, merci de changer tous les secrets sur votre disque. On continue les recherches sur le binaire et on va publier les dΓ©tails additionnels sous peu.
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JFPuget πŸ‡«πŸ‡·πŸ‡ΊπŸ‡¦πŸ‡¨πŸ‡¦πŸ‡¬πŸ‡± retweeted
a small announcement: I'm almost done with a puzzlescript-based benchmark; you can think of it as ARC-AGI-3.5, maybe even ARC-AGI-4; I expect it to be the most reliable way of assessing agents' abstract reasoning skills in a game environments I went through over 300 games, handpicked the most interesting and diverse set of 40, fixed a ton of bugs, went through multiple iterations of prompt design, etc most games can be completed in around 1h, some of them are significantly harder; models are tested using standard harnesses (codex/claude code) with no code generation allowed; action limits are very generous and unfortunately the benchmark is already somewhat saturated, but the models are still insanely action inefficient FYI, I won't release the game set to the public, but I guess I might share runs from some of the rejected games. btw, if anyone knows, what do I have to do in order to not pay $10K out of my own pocket for testing, I'm all ears
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I am relying more and more on AI agents, now they handle all my training experiments. I am still reading agent harness code, but for how long? Currently using codex with astra max. I've been burned by claude code slop (the TUI, not the models nor the output) to stay on codex. The main issue I have, tested with several models and harnesses, is that coding agents over engineer. I often have to make them roll back changes I did not ask for. They also keep dead code "for compatibility" when compatibility was never a concern. The main effect is to make the code harder to read. It also increase token consumption for no benefit. And no, software engineering is not solved. It is largely automated, but you still need to drive IMHO. That claude code team discovered that zipping text enables faster transmission in past few days is telling. If SWE was solved then this would have been done months ago.
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Account based in Canada. I lived in Paris in the 80s, no one ever sunbath or swim at Trocadero. AI images + propaganda.
Paris in the 1980s This is what they took from us
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JFPuget πŸ‡«πŸ‡·πŸ‡ΊπŸ‡¦πŸ‡¨πŸ‡¦πŸ‡¬πŸ‡± retweeted
One day we'll tell stories about how people thought language models were intelligent with the same incredulity of people thinking that cameras steal your soul, or that the train in the movie was actually going to crash into people.
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JFPuget πŸ‡«πŸ‡·πŸ‡ΊπŸ‡¦πŸ‡¨πŸ‡¦πŸ‡¬πŸ‡± retweeted
No you dumb fuck, some European students can do it easily.
This clip has more innovation than all of Europe has produced in 100 years
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JFPuget πŸ‡«πŸ‡·πŸ‡ΊπŸ‡¦πŸ‡¨πŸ‡¦πŸ‡¬πŸ‡± retweeted
Nemotron-Labs models give us a glimpse of what the future might be. And it may be looped! Dory1.0-4B is an impressive 4B model! huggingface.co/nvidia/NVIDIA…
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JFPuget πŸ‡«πŸ‡·πŸ‡ΊπŸ‡¦πŸ‡¨πŸ‡¦πŸ‡¬πŸ‡± retweeted
NVIDIA CEO @JensenHuang tells @altcap on @BG2Pod that people misunderstand Nvidia's demand. About a third is classical computing growing 30 to 40% a year with no AI labs. A quarter to a third is five or six frontier labs, not two. And a third is startups renting Nvidia like next-gen AWS. In my view, treating Nvidia as a two-lab capex story is a real mistake. "People misunderstand what's going on. There are basically three large segments. The first is baseline computing. Data processing, molecular dynamics, physics, simulation, computer graphics, classical machine learning, fraud detection, recommender systems. Classical computing, not AI, not deep learning, not agentic systems. Call it a third of our business. And that business is growing call it 30%, 40% per year. In the absence of any AI labs, Nvidia is not zero. We're quite a large company. The second part is the AI labs. People see two, but we work with every one of them. Meta Muse is doing fantastic. It's vastly Nvidia. Grok didn't exist three years ago. We didn't run Gemini before, we run Gemini now. Starting this year, we started working with Anthropic. People see a couple of labs, but I see more than that. I see five, six labs. And if you told me that represents not quite one-third of our business, let's say a quarter of our compute, fantastic. And another third, this is the part that's really great. Because we're at the beginning of a new computing platform shift, a vast number of new startups are coming out. Thousands of them. And what do they do? They rent NVIDIA compute. So a third of our business is kind of cloud computing for the next generation. It's kind of like AWS for today. And that represents about a third of our business altogether. My sense is that we're in the early innings of the buildout of this new computing architecture. And it's a real big mistake to recognize just a couple of companies and think that they were funding the infrastructure.”
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We are excited to announce that we post-trained NVIDIA Nemotron-3.5-Super-VL to be an autonomous ML researcher. We show that our post-trained model goes from 28% to 35% on AutoResearchExam, matching Qwen 3.8 Max, a model with 20Γ— the total parameters. The tasks are long horizon, open ended AI and ML research: building a faster executor for a transformer compute graph, training a RL control policy, creating new uncertainty quantification methods, etc. The model gets to submit once, and it doesn't see its score until the end. We curated SFT data using Self-context distillation. This means that we let the Nemotron model to solve a problem with hints, then we SFT on these trajectories but without the hints. This gave us +11% on AutoResearchExam. As a next step, we added reflection: We ask the Nemotron model to review its own past attempts and writes concrete lessons learned. Then we SFT on these reflections. Surprisingly, training on these lessons improved the performance to +25% compared to the base model. The hardest part was choosing the right data. We selected traces based on task scores, completion checks, and runtime, then refined the sampling across training rounds to address failures in the base and fine-tuned models. We removed hints and filtered out steps that relied on information available only in those hints. The goal was to teach decisions supported by the task and history the model would actually see at inference. Full write-up: bespokelabs.ai/blog/teaching… Thanks to @NVIDIAAI for early access and collaborating on this.
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Yes, we launched TWO models today! ✨✨ * Nemotron-3.5-Super for your DGX Sparks and H100s * Nemotron-3.5-Bolt for your RTX Sparks huggingface.co/nvidia/NVIDIA… huggingface.co/nvidia/NVIDIA…
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JFPuget πŸ‡«πŸ‡·πŸ‡ΊπŸ‡¦πŸ‡¨πŸ‡¦πŸ‡¬πŸ‡± retweeted
Mythos creating jobs, this must be that new economy I've heard so much about.
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JFPuget πŸ‡«πŸ‡·πŸ‡ΊπŸ‡¦πŸ‡¨πŸ‡¦πŸ‡¬πŸ‡± retweeted
Can you identify which LLM wrote a passage from the text alone? πŸ‘€ Each AI lab leaves behind distinct stylistic markers in vocabulary, phrasing, and punctuation. In Name That LLM, you'll identify which of 18 leading models from 6 organizations (Anthropic, Google, Moonshot AI, OpenAI, Qwen, and ZAI) wrote each unlabeled passage. πŸ’° Prize Pool: $50,000 ⏰ Entry Deadline: January 6, 2027
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JFPuget πŸ‡«πŸ‡·πŸ‡ΊπŸ‡¦πŸ‡¨πŸ‡¦πŸ‡¬πŸ‡± retweeted
LLMs are trained to provide average human response. When you provide context, it becomes average expert response. Sharp probability distribution hints that they are overfit to the training data. While it is very bad for most ML models, LLM use cases suffer less from it.
This is the least surprising graph in the world if you understand how gradient descent works.
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And Europe overpays for US weapons to be sent to Ukraine. The 10% premium should be discounted from what the US spent.
US has not spent $350B on Ukraine. It has spent $132B, $33B of that is money. The rest is $21B in loans, repaid from frozen Russian assets, $4B in humanitarian aid, and the vast majority, $74B is military equipment. Half that equipment is new, made in the US. The other half is old stockpile equipment, which soon would have had to be demilitarized at great cost. The replacement equipment is made in the US, so the money stays in the US. 
Meanwhile, the EU and European countries have spent a combined $271B, more than twice as much as the US.
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