today, i'm excited to release a reinforcement learning guide that carefully explains the intuition and implementation details behind every single fundamental algorithm in the field. enjoy :) naklecha.com/reinforcement-…

1/ AI is accelerating. But can we ensure that AIs truly share our values and follow our goals? We argue that aligning advanced AI systems requires cracking a core scientific challenge: how data shapes AI's internal structure, and how that structure determines behavior.

Introducing the Environments Hub RL environments are the key bottleneck to the next wave of AI progress, but big labs are locking them down We built a community platform for crowdsourcing open environments, so anyone can contribute to open-source AGI

This is one of the most underrated resources to learn CS fundamentals from scratch. It literally covers everything about CS you should know. teachyourselfcs.com

If you are into books, here is a list of all the essential books every developer must read!!!

Supplementary information for the new DeepSeek R1 Nature paper is very interesting! Details on training data, hyperparameters, base model importance, and more.

We present RAPTOR! 🚁 A tiny foundation policy flying any quadrotor 🦾 Tested on 10 real quadrotors from 32g to 2.4kg ⚡️ Adapts within milliseconds, zero-shot ⚙️ Runs inside PX4/Betaflight/etc.

For me, our Analyst agent in Microsoft 365 Copilot is like having a skilled data analyst on hand — all the time. It generates insights and helps you visualize complex data. Here's a quick demo on how it can make sense of marketing campaign data and help you get more done at

.Richard Sutton, father of reinforcement learning, doesn’t think LLMs are bitter-lesson-pilled. My steel man of Richard’s position: we need some new architecture to enable continual (on-the-job) learning. And if we have continual learning, we don't need a special training

Agents have transformed software, now it's time for robotics. Today, we’re revealing our blueprint for building agentic robots –– machines that can reason, converse, compose, and remember.

Our NeurIPS '25 Spotlight paper presents an *online* preference learning or RLHF method that enables agents to learn from human feedback in real-time — a step toward safer and more aligned AI for robots. Led by Haoyuan Cai and Zhenghao Peng Webpage: metadriverse.github.io/ppl/

should we release a comprehensive 70+ pages tutorial on robot learning, with hands-on code examples using LeRobot and Hugging Face? 🤗

Everyone wants to get into robotics. No one knows where to start. LeRobot's Francesco just dropped a 70-page crash course that takes you from zero to cutting-edge: - RL sim/real - ACT, Diffusion policies - VLAs, SmolVLA, Pi-0 Absolute gold if you want to catch up fast.

Introducing NotebookLM for arXiv papers 🚀 Transform dense AI research into an engaging conversation With context across thousands of related papers, it captures motivations, draws connections to SOTA, and explains key insights like a professor who's read the entire field

At UC Berkeley EECS we always work to keep our curriculum fresh. Our intro ML course CS 189 just got a drastic makeover this semester (thanks Joey Gonzalez Narges Norouzi!) and now includes ~12 lectures on e.g. Adam, PyTorch, various NN architectures, LLMs, and more (see

Strongly disagree with the original post, and agree with that Berkeley, Stanford, and UCSD actually do offer many good courses that are cutting edge and timely. For example, this Winter I offered this machine learning systems course hao-ai-lab.github.io/cse234-w25/ at UCSD (all materials

🚨 Hugging Face & Oxford just dropped the playbook for robot intelligence. It’s called LeRobot, and it’s basically the “PyTorch of robotics.” End-to-end code. Real hardware. Generalist robot policies. All open source. Here’s why this is huge: • Robots can now learn from data

Now releasing the course version of the robotics tutorial on the hub at huggingface.co/robotics-course

gold mine for anyone in robotics ⛏️

Open source has always been a huge part of robotics, and this feels more true now than ever, with a proliferation of innovative open-source hardware and software platforms that have made robotics more accessible and dynamic than ever before, even in an age of huge valuations: