Jack Monas

AI + World Models @1x_tech Prev. @Princeton @GoogleQuantumAI, @MSFTResearch

[dar @radbackwards]

It’s funny… 2 years later and now everyone’s talking about World Models.

Our robots were hallucinating shit with the 1X World Model back in ‘24.

Jack and the boys were ahead of their time.

[Jack Monas @JackMonas]

@radbackwards I won't forget the day the spoon fell back on the table

[Jack Monas @JackMonas]

This is going to be fun....excited to scale this up with you @_sam_sinha_!

[Samarth Sinha @_sam_sinha_]

I am SO excited to be sharing that I am joining @BerntBornich and @1x_tech to lead the new 1X World Model Lab aimed at building the next frontier of embodied AI! The core guiding principle of the lab is: scale up along every damn axis!! 🚀

Robotics data is NOT a second-class citizen - it is too important of a problem to be left to fine tuning! Your model needs to see your most important tokens from step 0

We need to think about robotics through the first principles of AI: how do we best utilize the vast amounts of web-scale media and how do we create a data-flywheel to collect millions of hours of rich robot interactions. There is no other moat in AI outside of data and @1x_tech has done an INCREDIBLE job scaling manufacturing, production and hardware to build humanoid robots that can create a unique data-flywheel in unstructured environments. Scaling data collection for highly dexterous on-policy robot data will be the only way for creating a moat in AI. @JackMonas and team have made great progress in building World Models, and now the goal is to supercharge this effort by starting a hyper-focused scale and data-pilled lab.

Before scaling compute / data / models, we are currently RAPIDLY scaling our team and hiring across the 4 core pillars of AI: model + data, data infra, ML infra and evals. Looking for folks that are excited about the 0->1 problem and share the same principles as us. There’s a single application for everyone in the lab - if you’re a good at engineering and ML, we will find a place for you in the team ❤️

AGI won’t be solved by fine-tuning… Let’s build the next frontier of AI together 🚀

My DMs are always open!!

[Eric Jang @ericjang11]

For the last few months I've been working on a from-scratch implementation of AlphaGo, a 2016 AI breakthrough that inspired me to get into deep learning. My casual understanding of AlphaGo was "search-augmented deep neural networks trained with self-play", but I wanted to go deeper and understand it by creating it.

Frontier deep learning research has always been expensive, but any given capability gets cheaper very quickly. In 2026, you no longer need DeepMind's resources to train a strong Go AI - you can vibe code all of it yourself for just a few thousand dollars of rented compute.

It was a huge honor to be invited to teach this with @dwarkesh_sp on @dwarkeshpodcast

I am an AlphaGo & Go apprentice, not a master, so all factual errors in the podcast are mine.

Web version of tutorial: https://t.co/Xkf9VsgtuT
Code: https://t.co/rWKOwclPDg
Play the go bot here: https://t.co/aVglJXldVX

[1X @1x_tech]

Building Your NEO

[Anirudha Majumdar @Majumdar_Ani]

https://t.co/3tzZESvF4L

[The Humanoid Hub @TheHumanoidHub]

Jitendra Malik rants about parallel-jaw grippers being inadequate; he believes multi-fingered hands with tactile sensing are necessary for advanced dexterous manipulation.

Malik is a Professor at UC Berkeley and a Distinguished Scientist at Amazon.

[Nicholas Pfaff @NicholasEPfaff]

Meet SceneSmith: An agentic system that generates entire simulation-ready environments from a single text prompt.

VLM agents collaborate to build scenes with dozens of objects per room, articulated furniture, and full physics properties.

We believe environment generation is no longer the bottleneck for scalable robot training and evaluation in simulation.

Website: https://t.co/UZklSkJe9V

👇🧵(1/8)

[Anirudha Majumdar @Majumdar_Ani]

https://t.co/Q8ZrYDfIxb

[Joel Jang @jang_yoel]

Introducing DreamZero 🤖🌎 from @nvidia

> A 14B “World Action Model” that achieves zero-shot generalization to unseen tasks & few-shot adaptation to new robots
> The key? Jointly predicting video & actions in the same diffusion forward pass

Project Page: https://t.co/qhygDzu6NY
🧵 (1/10)

[RoboPapers @RoboPapers]

Every home is different. That means that to build a useful home robot, we must be able to perform zero-shot generalization on a wide range of tasks. Humanoid company @1x_tech has a solution: world models.

1X Director of Evaluations @itsdanielho joins us on RoboPapers to talk about:
- why world models are the future for scaling robot learning
- how to use world models for robot control
- what world models unlock for evaluating robot model performance
- how we can hill-climb from here to general purpose robots

Watch Episode #61 of RoboPapers, with @micoolcho and @chris_j_paxton, now!

[Arash Vahdat @ArashVahdat]

🚀 Diffusion too slow? Fix it in a few steps.

📢 Introducing NVIDIA FastGen — a plug-and-play research library for turning slow diffusion models into high-quality few-step generators.

⚡ What’s inside:
• Consistency & MeanFlow (CM, sCM, TCM, MeanFlow)
• Distribution Matching (DMD, f-Distill, LADD)
• Long-video generation (CausVid, Self-Forcing)
• Fine-tuning & KD (SFT, CausalSFT, KD, Causal KD)

🧠 Includes:
📷 EDM, DiT, SD 1.5, SDXL, Flux
🎬 WAN (T2V / I2V / VACE), CogVideoX, Cosmos Predict2

✨ One unified interface. Research-ready. Apache-2.0.

🔗 Blog: https://t.co/rUO6V7WFoD
💻 Code: https://t.co/KpDN1ZiqrR

[Jack Monas @JackMonas]

@Miccooper9 Really cool!

[Moo Jin Kim @moo_jin_kim]

We release Cosmos Policy 💫: a state-of-the-art robot policy built on a video diffusion model backbone.

- policy + world model + value function — in 1 model
- no architectural changes to the base video model
- SOTA in LIBERO (98.5%), RoboCasa (67.1%), & ALOHA tasks (93.6%)

🧵👇

[Jack Monas @JackMonas]

@ericjang11 Eric the Mage, thank you for everything. It has been a pleasure working with you.

[Jack Monas @JackMonas]

@robotryer @1x_tech Interesting questions. The user should be able to define behaviors. We have some work to do to figure out the “how” so stay tuned.

[Jack Monas @JackMonas]

One of many next steps at @1x_tech: preference learning for world-model-based policies.

Given a generated starting frame, we can sample multiple video rollouts from our WM and use preference feedback to steer the model toward higher-quality behavior.

This lets us fix policy failures in synthetic worlds—resolving bad NEO behaviors with generated dogs before we ever meet real ones.

[Jack Monas @JackMonas]

@bobertorien @1x_tech NEO's specialty is belly rubs

[Jack Monas @JackMonas]

@fatemi_michael Your work during your time at 1X helped lay the foundation for this release. Thank you Michael!

[Jack Monas @JackMonas]

@_advaitpatel @peterliuposts 🐐