Kristian Georgiev (@kris_georgiev1) profile picture

Research Scientist @OpenAI

San Francisco, CA
Joined April 2020
Kristian Georgiev retweeted
We’re releasing a broad range of new mathematical results produced by an internal frontier model. We’ve been consulting with the independent Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study, and we have drawn on their advice and public recommendations to inform how we release these results. github.com/openai/math
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Kristian Georgiev retweeted
We’re giving scientists, mathematicians, and engineers free access to our frontier models—starting with 10,000 researchers and expanding to 100,000 through 2027. ChatGPT for Academic Researchers is built to accelerate discovery across disciplines.
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Kristian Georgiev retweeted
AI can give researchers the freedom to pursue “crazier” ideas. For Terence Tao, AI creates more room to experiment, test unexpected paths, and discover what might otherwise stay out of reach.
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Kristian Georgiev retweeted
Introducing GPT-5.5 A new class of intelligence for real work and powering agents, built to understand complex goals, use tools, check its work, and carry more tasks through to completion. It marks a new way of getting computer work done. Now available in ChatGPT and Codex.
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Kristian Georgiev retweeted
In my doctorate, I proved the Erdős Primitive Set Conjecture, showing that the primes themselves are maximal among all primitive sets. This problem will always be in my heart: I worked on it for 4 years (even when my mentors recommended against it!) and loved every minute of it. [Primitive sets are a vast generalization of the prime numbers: A set S is called primitive if no number in S divides another.] Now Erdős#1196 is an asymptotic version of Erdős' conjecture, for primitive sets of "large" numbers. It was posed in 1966 by the Hungarian legends Paul Erdős, András Sárközy, and Endre Szemerédi. I'd been working on it for many years, and consulted/badgered many experts about it, including my mentors Carl Pomerance and James Maynard. The the proof produced by GPT5.4 Pro was quite surprising, since it rejected the "gambit" that was implicit in all works on the subject since Erdős' original 1935 paper. The idea to pass from analysis to probability was so natural & tempting from a human-conceptual point of view, that it obscured a technical possibility to retain (efficient, yet counter-intuitve) analytic terminology throughout, by use of the von Mangoldt function \Lambda(n). The closest analogy I would give would be that the main openings in chess were well-studied, but AI discovers a new opening line that had been overlooked based on human aesthetics and convention. In fact, the von Mangoldt function itself is celebrated for it's connection to primes and the Riemann zeta function--but its piecewise definition appears to be odd and unmotivated to students seeing it for the first time. By the same token, in Erdős#1196, the von Mangoldt weights seem odd and unmotivated but turn out to cleverly encode a fundamental identity \sum_{q|n}\Lambda(q) = \log n, which is equivalent to unique factorization of n into primes. This is the exact trick that breaks the analytic issues arising in the "usual opening". Moreover, Terry Tao has long suspected that the applications of probability to number theory are unnecessarily complicated and this "trick" might actually clarify the general theory, which would have a broader impact than solving a single conjecture.
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Kristian Georgiev retweeted
GPT-5 is here. Rolling out to everyone starting today. openai.com/gpt-5/
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It's happening ...
A fourth problem on FrontierMath Tier 4 has been solved by AI! Written by Dan Romik, it had won our prize for the best submission in the number theory category.
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Kristian Georgiev retweeted
Why do larger language models generalize better? In our new ICLR paper, we derive an interpretable generalization bound showing that compute-optimal LLMs provably generalize better with scale! 📄arxiv.org/abs/2504.15208 1/7🧵
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Kristian Georgiev retweeted
We’ve been hard at work on reinforcement fine-tuning (RFT) to make it a more flexible and powerful tool. RFT is best thought of as a way to improve model capabilities on well-specified tasks with known correct answers. It shouldn't come as a surprise that models can often get much better at specific tasks when they are trained directly on those tasks. Picture many plots going up and to the right! I'm excited for more people to try it soon.
We worked with the @OpenAI  team to test and evaluate fine-tuned reasoning models in SafetyKit and saw great results, especially with complex instruction following and large context windows. SafetyKit customers need lots of expert-level decision-making across lots of complex policy areas. OpenAI’s reinforcement fine-tuning lets us replace a bunch of nodes in our most complex workflows with a single o3-mini reasoning agent. That simplification lets us expand across policy areas much more quickly, and that’s what our customers need most.
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Kristian Georgiev retweeted
Want state-of-the-art data curation, data poisoning & more? Just do gradient descent! w/ @andrew_ilyas Ben Chen @axel_s_feldmann @wsmoses @aleks_madry: we show how to optimize final model loss wrt any continuous variable. Key idea: Metagradients (grads through model training)
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Kristian Georgiev retweeted
large reasoning models are extremely good at reward hacking. A thread of examples from OpenAI's recent monitoring paper: (0/n)
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le chat doesn’t disappoint
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Kristian Georgiev retweeted
Replying to @karpathy
The hottest new programming language is vibes
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Kristian Georgiev retweeted
Clean mathematical explanation for why critical windows appear for any localization-based sampler! Also interesting that in LLMs, these often correspond to failures in reasoning. Congrats @marvin_li03 and @aakaran31 for the cool mix of theory and empirical work!
New paper alert!🚨 What do LLM reasoning, diffusions, & jailbreaks have in common? 🤔 All exhibit critical windows📈--a sudden formation of distinctive features during sampling, e.g. correctness or toxicity. We present a unifying theory of critical windows for diffusion & LLMs.
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Kristian Georgiev retweeted
Excited to see this new paper published in @TmlrOrg! I study the problem of simultaneously estimating many private regressions that share the same set of covariates X but have l different outcomes Y. For example, X might be a persons genomic data, and Y's might correspond to different disease statuses. Can we do better than naively composing over l different DP regressions (incurring sqrt(l) blowup in error)?
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Kristian Georgiev retweeted
I wrote a survey article on computationally efficient methods for "robust" mean estimation, which includes robustness to contamination, heavy-tailed data, or in the sense of differential privacy. The same ideas are useful for all 3 (seemingly-different) forms of robustness! 1/2
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Kristian Georgiev retweeted
DMM is a *meta-algorithm*, so better data attribution ➡️ better oracle predictions ➡️ better unlearning! Check out our work for details on DMM, new techniques for evaluating unlearning, theoretical analyses, and more! arXiv: arxiv.org/abs/2410.23232 Blog: bit.ly/unlearning-via-datamo…
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Kristian Georgiev retweeted
Machine unlearning ("removing" training data from a trained ML model) is a hard, important problem. Datamodel Matching (DMM): a new unlearning paradigm with strong empirical performance! w/ @kris_georgiev1 @RoyRinberg @smsampark @shivamg_13 @aleks_madry @SethInternet (1/4)
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Kristian Georgiev retweeted
ContextCite Attributing Model Generation to Context paper page: huggingface.co/papers/2409.0… How do language models use information provided as context when generating a response? Can we infer whether a particular generated statement is actually grounded in the context, a misinterpretation, or fabricated? To help answer these questions, we introduce the problem of context attribution: pinpointing the parts of the context (if any) that led a model to generate a particular statement. We then present ContextCite, a simple and scalable method for context attribution that can be applied on top of any existing language model. Finally, we showcase the utility of ContextCite through three applications: (1) helping verify generated statements (2) improving response quality by pruning the context and (3) detecting poisoning attacks.
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Kristian Georgiev retweeted
The ATTRIB workshop is back @ NeurIPS 2024! We welcome papers connecting model behavior to data, algorithms, parameters, scale, or anything else. Submit by Sep 18! More info: attrib-workshop.cc Co-organizers: @tolgab0 @logan_engstrom @SadhikaMalladi @_elinguyen @smsampark
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