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Mouhssine Rifaki

@smrifaki.bsky.social
38 followers 305 following 0 posts

incoming phd in rl at imperial | nyu | stanford | mva at ens paris-saclay

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Reposted by Mouhssine Rifaki
Nathan Lambert @natolambert.bsky.social · 11/04/2026
In 5-10 years, as models get more expensive & capable, I see the funding structures and support for open models breaking down. We need to consider if we need other options of supporting the open ecosystem. The inevitable need for an open model consortium www.interconnects.ai/p/the-inevit...
interconnects.ai
The inevitable need for an open model consortium
And yes, I hate consortia too.
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Jeff Dean @jeffdean.bsky.social · 04/02/2025
Training our most capable Gemini models relies heavily on our JAX software stack+Google's TPU hardware platforms. If you want to learn more, see this awesome book "How to Scale Your Model": jax-ml.github.io/scaling-book/ Put together by several of my Google DeepMind colleagues listed below 🎉.
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Alexandra Keinath @atkeinath.bsky.social · 08/12/2025
Another new paper from the lab: Predictive theories like the SR imply that navigators who navigate differently should have cognitive maps which differ in predictable ways. Here we show that this holds in mouse hippocampal CA1. www.sciencedirect.com/science/arti...
sciencedirect.com
Environmental representations in mouse hippocampal CA1 reflect the predictive structure of navigation
Predictive theories of cognitive mapping propose that these representations encode the predictive relationships among contents as experienced by the n…
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Gizem Özdil @gzmozd.bsky.social · 12/09/2025
🪰 How do dozens of tiny fly muscles cooperate to move a leg? We’re excited to share the first 3D, data-driven musculoskeletal model of Drosophila legs based on Hill-type muscles, running in OpenSim and MuJoCo simulation environments. Preprint: arxiv.org/abs/2509.06426
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Jun-Yan Zhu @junyanz.bsky.social · 10/05/2025
[1/2] We've released the code for LegoGPT. Our autoregressive model generates physically stable and buildable designs from text prompts by integrating physics laws and assembly constraints into LLM training and inference. Code: github.com/AvaLovelace1... Website: avalovelace1.github.io/LegoGPT/
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Eugene Vinitsky 🍒 @eugenevinitsky.bsky.social · 09/11/2024
If you're an RL researcher or RL adjacent, pipe up to make sure I've added you here! go.bsky.app/3WPHcHg
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Dhruv Batra @dhruvbatra.bsky.social · 14/12/2024
Brilliant talk by Ilya, but he's wrong on one point. We are NOT running out of data. We are running out of human-written text. We have more videos than we know what to do with. We just haven't solved pre-training in vision. Just go out and sense the world. Data is easy.
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Andreas Geiger @andreasgeiger.bsky.social · 14/04/2025
🚀 Never miss a beat in science again! 📬 Scholar Inbox is your personal assistant for staying up to date with your literature. It includes: visual summaries, collections, search and a conference planner. Check out our white paper: arxiv.org/abs/2504.08385 #OpenScience #AI #RecommenderSystems
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Simon Willison @simonwillison.net · 26/06/2025
I'm really impressed by the new Gemma 3n I tried a 7.5GB model from Ollama and a 15GB model through mlx-vlm - they seem very capable, and this is the first model of that size I've tried that can handle both image AND audio input in addition to text! simonwillison.net/2025/Jun/26/...
simonwillison.net
Introducing Gemma 3n: The developer guide
Extremely consequential new open weights model release from Google today: Multimodal by design: Gemma 3n natively supports image, audio, video, and text inputs and text outputs. Optimized for on-devic...
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Eugene Vinitsky 🍒 @eugenevinitsky.bsky.social · 03/09/2025
Reminded again of Kevin Murphy's excellent RL overview: arxiv.org/abs/2412.05265 A lot of the stuff covered here really is at the cutting edge and not compiled so nicely anywhere else
arxiv.org
Reinforcement Learning: An Overview
This manuscript gives a big-picture, up-to-date overview of the field of (deep) reinforcement learning and sequential decision making, covering value-based methods, policy-based methods, model-based m...
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Karas Lab @karaslab.bsky.social · 15/03/2025
Newest preprint from our lab. Congratulations to all authors!
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Ida Momennejad @neuroai.bsky.social · 14/10/2023
As my first post, delighted to share our #neurips2023 paper: Evaluating Cognitive Maps & Planning in LLMs with CogEval We test cognitive maps & planning in 8 LLMs. Failures like hallucinating invalid paths & falling in loops suggest no emergent zero-shot planning. 1/n 🧵 arxiv.org/abs/2309.15129
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Max Kleiman-Weiner @maxkw.bsky.social · 22/07/2025
Our new paper is out in PNAS: "Evolving general cooperation with a Bayesian theory of mind"! Humans are the ultimate cooperators. We coordinate on a scale and scope no other species (nor AI) can match. What makes this possible? 🧵 www.pnas.org/doi/10.1073/...
pnas.org
Evolving general cooperation with a Bayesian theory of mind | PNAS
Theories of the evolution of cooperation through reciprocity explain how unrelated self-interested individuals can accomplish more together than th...
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Gabriel Peyré @gabrielpeyre.bsky.social · 15/01/2025
Slides for a general introduction to the use of Optimal Transport methods in learning, with an emphasis on diffusion models, flow matching, training 2 layers neural networks and deep transformers. speakerdeck.com/gpeyre/optim...
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Angelina Wang @ COLM @angelinawang.bsky.social · 17/02/2025
Our new piece in Nature Machine Intelligence: LLMs are replacing human participants, but can they simulate diverse respondents? Surveys use representative sampling for a reason, and our work shows how LLM training prevents accurate simulation of different human identities.
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Pablo Samuel Castro @pcastr.bsky.social · 10/02/2025
Can LLMs be used to discover interpretable models of human and animal behavior?🤔 Turns out: yes! Thrilled to share our latest preprint where we used FunSearch to automatically discover symbolic cognitive models of behavior. 1/12
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Andrew Saxe @saxelab.bsky.social · 03/02/2026
Why don’t neural networks learn all at once, but instead progress from simple to complex solutions? And what does “simple” even mean across different neural network architectures? Sharing our new paper @iclr_conf led by Yedi Zhang with Peter Latham arxiv.org/abs/2512.20607
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Simon Willison @simonwillison.net · 23/11/2025
Olmo 3 is notable as a "fully open" LLM - all of the training data is published, plus complete details on how the training process was run. I tried out the 32B thinking model and the 7B instruct models, + thoughts on why transparent training data is so important simonwillison.net/2025/Nov/22/...
simonwillison.net
Olmo 3 is a fully open LLM
Olmo is the LLM series from Ai2—the Allen institute for AI. Unlike most open weight models these are notable for including the full training data, training process and checkpoints along …
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Daniel Lowd @dlowd.com · 17/11/2024
Since this platform is finally attracting a critical mass of ML researchers, here's our recent work on prompt-based vulnerabilities of coding assistants: arxiv.org/abs/2407.11072 TL;DR — An attacker can convince your favorite LLM to suggest vulnerable code with just a minor change to the prompt!
arxiv.org
MaPPing Your Model: Assessing the Impact of Adversarial Attacks on LLM-based Programming Assistants
LLM-based programming assistants offer the promise of programming faster but with the risk of introducing more security vulnerabilities. Prior work has studied how LLMs could be maliciously fine-tuned...
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Mick Bonner @mickbonner.bsky.social · 11/12/2025
Dimensionality reduction may be the wrong approach to understanding neural representations. Our new paper shows that across human visual cortex, dimensionality is unbounded and scales with dataset size—we show this across nearly four orders of magnitude. journals.plos.org/ploscompbiol...
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