Yuandong Tian

Recursive

@tydsh

Co-founder of @Recursive_SI. ex-Meta FAIR Director. ex-Google. Reasoning, Optimization and Understanding LLM. Novelist in spare time. PhD in @CMU_Robotics.

California, USA yuandong-tian.com Joined December 2009

954 Following 48K Followers 1.2K Posts

Latest posts 5

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Yuandong Tian @tydsh Oct 21

Today, we release our first model, Flint-40B-A2B, a fast local coding model built with our automated research system. huggingface.co/RecursiveSI/fl…

1. Strong performance. 54.9% on Terminal-Bench 2.1, 89.7% on SWE-bench Pro v2, and 74.2% on SWE-bench Verified.

2. It is trained from scratch (tokenizer->Pretrain->Midtrain->SFT->RL).

3. Fast in your local laptop. Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark.

4. We build the complete model training pipeline in 4 month!

huggingface.co RecursiveSI/flint · Hugging Face Text Generation model with 40B parameters. Download, explore files and run it on Hugging Face.

Richard Socher Verified account @RichardSocher Oct 21

Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine.

• Best-in-class fast local coding
• Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark
• Built from scratch in under 4 months

Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.

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Yuandong Tian @tydsh 3d

Great to see @Recursive_SI ranked #7 for Top Startup that attracts top talents!

Deedy Verified account @deedydas 4d

Top 30 Startups and BigTech companies based purely on ~50,000 engineer job changes in the last 3 years.

If engineers leave a company to join another, it ranks higher especially if the company they come from ranks highly.

Here are the final results:

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Yuandong Tian @tydsh Nov 3

Interesting work! I wonder whether this also works for other models than Qwen3.5-0.8B? It could be possible that the mech interp analysis of the Qwen model has been incorporated into the training set of Astra.

E.g., can this approach make our recently released Flint 40B-A2B model better 😀

There could be also simple reward hacking like closing the thinking mode of the model to avoid hitting the thinking tokens limit..

Rikiya Takehi @rikiyatakehi Nov 2

We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math.
It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training.

We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵
Blog: rikiyat.github.io/blog/weight-sp…

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Yuandong Tian @tydsh Oct 21

👉👉 AI Scientific Discovery Demos with our Recursive Flint-40B-A2B 🪨🪨!

Cong Lu Verified account @cong_ml Oct 21

1/ From OpenAI's Navier–Stokes work to AlphaEvolve's faster algorithms, AI research is getting very real.

Now, with Recursive Flint, we can experience that magic locally on our own laptops 💻

The whole research loop runs locally with a selection of tasks! Code + demos 🧵

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Yuandong Tian @tydsh Oct 17

A great read. The blog post captures years of progress in mechanistic interpretability and offers an optimistic vision: perhaps we really can open the black box and understand how neural networks work.

I share that optimism, but with some reservations.

I'm skeptical of explanations that stop at individual neurons, accompanied by compelling visualizations that seem to "explain everything."

Humans are remarkably good at convincing themselves. It's easy to cherry-pick evidence that supports a hypothesis, especially when the visualizations look convincing.

I'm also not convinced that "Recursive Generalization" is fundamentally different from hierarchical representation learning. As representations become more abstract, they discard details while preserving invariances. Isn't that essentially what hierarchical representations have always been about?

IMHO, we need to move beyond a largely descriptive, behavioral approach toward a principled mathematical framework. And we have a natural starting point: gradient descent.

Rather than just asking what a network has learned, we should understand why learning dynamics produce these structures in the first place.

Of course, this is extraordinarily difficult. Look at OpenAI's 719 mathematical proofs. We see plausible proofs tackling problems at the Fields Medal level, yet no comparable breakthrough that cracks deep learning theory. That contrast is striking.

Part of the difficulty is that there are countless plausible theories of how deep networks work. Proving even one can be mathematically formidable. And a proof alone isn't enough. Each theory needs empirical validation, often requiring substantial GPU compute. It's a daunting combination of mathematical and experimental challenges.

Still, I believe this is the way forward. Interpretability gives us observations and hypotheses. But ultimately, we need a theory that explains why these phenomena emerge from the learning process itself.

That's how we turn compelling stories about neural networks into actual understanding. And hopefully recursive self-improvement can help with that 😀

Nick Verified account @nickcammarata Oct 15

interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had

with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works

already-clean.goodfire.com
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