nathan lile

Superintelligence isn't about discovering new things; it's about discovering new ways to discover I think our latest work formalizes Meta Chain-of-Thought which we believe lies on the path to ASI When we train models on the problem-solving process itself—rather than the final solution—they internalize how to think about reasoning tasks, not just what to think The next wave of AI is a Meta-CoT loop. We can't predict what novel forms of thinking might emerge, but it points to an extraordinary synthetic future I'm so proud of @synth_labs team & our incredible open science collaborators for getting this work out

@zaph0id @0xyaza @DSPyOSS @lateinteraction #L1" target="_blank" rel="nofollow noopener">github.com/stanfordnlp/ds…

@0xyaza @DSPyOSS So RLM is now in @DSPyOSS ?? @lateinteraction ?

The RLM implementation and usage in @DSPyOSS is beautiful.

we’re at #COLM2025🍁 come see our poster # 26 (session 1) today reach out ✉️ if you'd like to chat!

🚨🚨New Paper: Training LLMs to Discover Abstractions for Solving Reasoning Problems Introducing RLAD, a two-player RL framework for LLMs to discover 'reasoning abstractions'—natural language hints that encode procedural knowledge for structured exploration in reasoning.🧵⬇️

much more convinced after getting my own results: LoRA with rank=1 learns (and generalizes) as well as full-tuning while saving 43% vRAM usage! allows me to RL bigger models with limited resources😆 script: github.com/sail-sg/oat/bl…

I’ll take the opposite view - current methods are saturating and we need at least 1 practical breakthrough and at least two fundamental ones (which will likely take years) just off the top of my head to reach AGI. None of these are oversight or safety related.

@parafactual @whybyfire very cool!

@whybyfire the model is presented with a reddit submission and a single comment thread inside that submission. the usernames are all redacted. the task is to identify which comments in the thread are from the same user

I fucking obliterated a 32b base model s brain

NEW: Is the internet changing our personalities for the worse? Conscientiousness and extroversion are down, neuroticism up, with young adults leading the charge. This is a really consequential shift, and there’s a lot going on here, so let’s get into the weeds 🧵

Really great collaborating with @NathanThinks! Reach out if you're working on synthetic data generation, offline RL, or simulating agentic behavior.

@_jasonwei excellent as always!

New blog post about asymmetry of verification and "verifier's law": jasonwei.net/blog/asymmetry… Asymmetry of verification–the idea that some tasks are much easier to verify than to solve–is becoming an important idea as we have RL that finally works generally. Great examples of asymmetry of verification are things like sudoku puzzles, writing the code for a website like instagram, and BrowseComp problems (takes ~100 websites to find the answer, but easy to verify once you have the answer). Other tasks have near-symmetry of verification, like summing two 900-digit numbers or some data processing scripts. Yet other tasks are much easier to propose feasible solutions for than to verify them (e.g., fact-checking a long essay or stating a new diet like "only eat bison"). An important thing to understand about asymmetry of verification is that you can improve the asymmetry by doing some work beforehand. For example, if you have the answer key to a math problem or if you have test cases for a Leetcode problem. This greatly increases the set of problems with desirable verification asymmetry. "Verifier's law" states that the ease of training AI to solve a task is proportional to how verifiable the task is. All tasks that are possible to solve and easy to verify will be solved by AI. The ability to train AI to solve a task is proportional to whether the task has the following properties: 1. Objective truth: everyone agrees what good solutions are 2. Fast to verify: any given solution can be verified in a few seconds 3. Scalable to verify: many solutions can be verified simultaneously 4. Low noise: verification is as tightly correlated to the solution quality as possible 5. Continuous reward: it’s easy to rank the goodness of many solutions for a single problem One obvious instantiation of verifier's law is the fact that most benchmarks proposed in AI are easy to verify and so far have been solved. Notice that virtually all popular benchmarks in the past ten years fit criteria #1-4; benchmarks that don’t meet criteria #1-4 would struggle to become popular. Why is verifiability so important? The amount of learning in AI that occurs is maximized when the above criteria are satisfied; you can take a lot of gradient steps where each step has a lot of signal. Speed of iteration is critical—it’s the reason that progress in the digital world has been so much faster than progress in the physical world. AlphaEvolve from Google is one of the greatest examples of leveraging asymmetry of verification. It focuses on setups that fit all the above criteria, and has led to a number of advancements in mathematics and other fields. Different from what we've been doing in AI for the last two decades, it's a new paradigm in that all problems are optimized in a setting where the train set is equivalent to the test set. Asymmetry of verification is everywhere and it's exciting to consider a world of jagged intelligence where anything we can measure will be solved.

@eshear cc @QuentinAnthon15 x.com/QuentinAnthon1…

METR’s analysis of this experiment is wildly misleading. The results indicate that people who have ~never used AI tools before are less productive while learning to use the tools, and say ~nothing about experienced AI tool users. Let's take a look at why. x.com/METR_Evals/sta…

@kalomaze yes pls!

if anyone wants to play around with this endpoint i can share a link

@higgsfield_ai ID

up _and_ left 😲

you have no idea how hard it is to get an rlhf model to be even “centrist” much less right reactionary. they must have beat this guy up pretty hard

I’m not big on identities, but I am extremely proud to be American. This is true every day, but especially today—I firmly believe this is the greatest country ever on Earth. The American miracle stands alone in world history. I believe in techno-capitalism. We should encourage people to make tons of money and then also find ways to widely distribute wealth and share the compounding magic of capitalism. One doesn’t work without the other; you cannot raise the floor and not also raise the ceiling for very long. The world should get richer every year through science and technology, but everyone has to be in the “up elevator”. I think the government usually does a worse job than markets, and so we need to encourage our culture of innovation and entrepreneurship. I also believe that education is critically important to keeping the American edge. I believed this when I was 20, when I was 30, and now I am 40 and still believe it. The Democratic party seemed reasonably aligned with it when I was 20, losing the plot when I was 30, and completely to have moved somewhere else at this point. So now I am politically homeless. But that’s fine; I care much, much more about being American than any political party. I’d rather hear from candidates about how they are going to make everyone have the stuff billionaires have instead of how they are going to eliminate billionaires. The American experiment has always been messy. I am hopeful for another great 250 years. Happy 4th!

Apple dropping diffusion based Coding LLMs on Hugging Face was not on my bingo

Xiaomi got 200,000 orders in 3 minutes for the YU7 and I’m not even surprised. The value proposition is just nuts. I’m kinda of bummed because it means a few more years of having to satisfy demand from China before global expansions.

the future is about smart tokens

What if models could learn which problems _deserve_ deep thinking? No labels. Just let the model discover difficulty through its own performance during training. Instead of burning compute 🔥💸 on trivial problems, it allocates 5x more on problems that actually need it ↓

@yacineMTB lmfao wish x had a trends dashboard of 📈📉 ‘user`s posts as ‘seen_by_my_timeline’ over ‘365 | …’ sometimes catches me off guard who silently crashes out

Someone should fire roon so he can post again