John Schulman
@johnschulman2
Recently started @thinkymachines. Interested in reinforcement learning, alignment, birds, jazz music
1.8K Following 74.5K Followers
Seeing the demos come together over the last week has been awesome -- so many things that previously required a special-purpose model (e.g. real-time translation, event detection in video) turn out to be zero-shot instruction following once you have a general-purpose model with the right type signature -- continuous/simultaneous audio+video+text->audio+text
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Sharing our work on full-duplex multimodal models -- real-time interaction that's natural and intuitive without compromising on intelligence.
We started Thinky in part to differentially advance capabilities for human-AI collaboration, which are underemphasized relative to intelligence/autonomy because they're harder to eval.
In the future, we think every AI system will have something like an interaction model as the outer user-facing layer, continually keeping the user informed and learning what they actually want.
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People talk, listen, watch, think, and collaborate at the same time, in real time. We've designed an AI that works with people the same way.
We share our approach, early results, and a quick look at our model in action.
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Luke and Rudolf's writing on keeping humans central in an AI-powered world sparked a lot of discussion at Thinking Machines. For me, it captured some things I'd been thinking about but hadn't put as clearly. The more I got to know them and learned about their work, the more I wanted to work together. Really glad they're joining us.
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Workshop Labs is joining @thinkymachines.
We believe there's a path for AI to make humans matter more. We couldn’t be prouder to join Thinking Machines to see this work through.
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Great work by Chroma training a search agent with SoTA efficiency. Lots of cool details: a prune tool for editing context mid-search, a synthetic data pipeline with verification steps, and a curriculum that shifts from recall to precision. Trained with Tinker!
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Introducing Chroma Context-1, a 20B parameter search agent.
> pushes the pareto frontier of agentic search
> order of magnitude faster
> order of magnitude cheaper
> Apache 2.0, open-source
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Models that are great at calibrated predictions will be transformative for decision making. Excited about Mantic's work and proud they're using Tinker. Their new blog post digs into their methodology and findings.
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I always dreamed of AGI as a wise advisor for humanity. Although LLMs are great for coding & knowledge work, I wouldn’t trust them to give me advice on my career, business strategy, or policy preferences. How can we build AI systems optimized for wisdom?
At Mantic we believe the unlock is prediction: predicting world events as accurately as possible, and hill-climbing this single metric.
Today we share some recent progress on the Thinking Machines website, having found Tinker a great platform for our RL experiments.
TL;DR: We RL-tune gpt-oss-120b to become a better forecaster than any other model. Having good scaffolding is a prerequisite. A fun result: our tuned model + Grok are decorrelated from the other best models, and so are the most indispensable when picking a team.
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I joined @thinkymachines to work with @johnschulman2, @miramurati, @soumithchintala, @dchaplot, @alexgartrell, and others on the future of collaborative AI!
And I can confirm that we do have 45 lb weights.
If you're interested in training weights and/or Tinkering with us, join us!
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We’ve loved watching the Tinker community grow, and we're excited to have a place to share product updates, helpful recipes, and spotlights on the amazing things Tinkerers are building.
Get started with Tinker here:
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Humans are jagged, and organizations (from companies to civilizations) have evolved as harnesses to make best use of us despite our faults
Weirdly, I actually think Yann is making an important point here that is getting lost in semantics.
Human intelligence also has jagged frontiers, we're just used to the shape.
Michael asked great questions -- thanks for having me on.
A conversation with @johnschulman2 on the first year LLMs could have been useful, building research teams, and where RL goes from here.
00:20 - Speedrunning ChatGPT
09:22 - Archetypes of research managers
11:56 - Was OpenAI inspired by Bell Labs?
16:54 - The absence of value functions
18:23 - Continual learning
21:09 - Brittle generalization
24:05 - Co-training generators and verifiers, GANs
27:06 - John’s personal use of AI for research
28:54 - Day in the life
33:01 - Slowdowns in consequential ML ideas
36:21 - "Peer review" within the labs
39:19 - Distribution shift in researchers
43:33 - Future of RL
45:33 - Will the labs coordinate if the world needs them to?
44:46 - Forecasting ills in AGI and engineering
47:53 - Thinking Machines
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Tinker is now generally available. We also added support for advanced vision input models, Kimi K2 Thinking, and a simpler way to sample from models.
Congratulations to @axiommathai on their achievement!
AxiomProver, a mathematics model fine-tuned with Tinker, got top scores on the Putnam Math Competition.
I am pleased to announce another update to my RL tutorial ( This time I have added code for RLFT for multi-turn LLM agents, using the awesome Tinker library from @thinkymachines, and the simple ReBN training loop from GEM by @zzlccc et al. With ~100 lines of simple python running on your laptop, you can train an agent based on Qwen3-4B-Instruct to play "guess the number" in 20 minutes.
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it's good to be back to the age of blogging
Today, OpenAI is launching a new Alignment Research blog: a space for publishing more of our work on alignment and safety more frequently, and for a technical audience.
Excited to announce that I’m releasing my first fiction collection, The Gentle Romance: Stories of AI and Humanity.
It contains 22 stories about our possible science fiction futures, and it’s out December 12.
See below for more details and the preorder link:
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AI that is “forced to be good” v “genuinely good”
Should we care about the difference? (yes!)
We’re releasing the first open implementation of character training. We shape the persona of AI assistants in a more robust way than alternatives like prompting or activation steering.
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Happy to share a new paper! Designing model behavior is hard -- desirable values often pull in opposite directions. Jifan's approach systematically generates scenarios where values conflict, helping us see where specs are missing coverage and how different models balance tradeoffs.
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New research paper with Anthropic and Thinking Machines
AI companies use model specifications to define desirable behaviors during training. Are model specs clearly expressing what we want models to do? And do different frontier models have different personalities?
We generated thousands of scenarios to find out. 🧵
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I am super excited to share a new AI tool, Refine.
Refine thoroughly studies research papers like a referee and finds issues with correctness, clarity, and consistency.
In my own papers, it regularly catches problems that my coauthors and I missed.
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