Ensuring that tech companies don't have a monopoly on being able to do research on cutting edge AI @AiEleuther. She/her
Joined May 2019
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If you follow me and haven’t listened to this talk in its entirety, doing so is more important than whatever it is you are currently doing. There’s a lot of cybersec jargon at times but I don’t think you need to know anything about security to understand what’s important.
Yesterday, my OpenAI collaborator and I gave a detailed talk on the Huggingface incident, our models creating "the message board", model misalignment, and more.
youtube.com/watch?v=87DyyMV0…
I hope it can answer a lot of the questions folks have, and we will release a full detailed postmortem at a later time!
Actually this is why we carefully curate all LLM training text to be pre-2022, to avoid poisoning our models with dirty LLM outputs.
This is actually a highly unusual circumstance that doesn't hold for any other planet in the solar system and (AFAIK) has no other known examples in the universe.
One interesting consequence is that this is the only known planet with total lunar AND solar eclipses.
So… we just have AGI now right? Like OpenAI’s internal model is smart enough to solve the vast majority of problems someone might care about, given recent results. It’s just a matter of connecting it up right, data collection, etc. The intelligence part is solved.
Huge congrats! Formal verification of proofs that are too long for one person* to read & understand is a big deal.
Note: Axiom emailed me a few days ago and asked me to promote this. Normally I refuse, but this is something I would have promoted if they didn’t ask me to.
Incredibly based article. Nobody is holding a gun to your head and telling you to end the world faster. You can just not do the bad thing.
I agree with meta-point that people get way too obsessed with “game theory” and “incentive structures” at times too. You can just be good.
This is a good and important piece, with plenty of spicy takes and language for good measure.
Gift link:
vox.com/future-perfect/50787…
The latest paper from our summer internship program chips away at the mysteries of Subliminal Learning through careful experimental design and analysis. Really interesting and thought-provoking results.
Some interesting thoughts about the paper from Chris here
Thanks for sharing this! It's been fascinating to try to piece together the story of subliminal learning, and it is very useful to have new experimental reports to help with this. A few quick comments:
1. The "Phantom transfer" paper reports positive results for subliminal effects with differently initialized models as well: arxiv.org/abs/2602.04899 (Cloud et al. also report a positive result in this space, for GPT-4.1 and GPT-4o, which are surely related models but which were not the same at the start of the SL experiments.)
2. It is easy to construct toy cases where different initializations lead to subliminal effects in the wrong direction for simple linear models, and it could be much worse for real LMs. Do you have any insights into how you have managed to avoid such situations?
3. Have you actually avoided such situations? The numbers for cross-model transfer seem pretty mixed. Are Figures 4B, A19B, and A26 really pictures of cross-model transfer? The effects seem weak at best to me. Maybe I am looking at this through the wrong lens?
4. Your results provide a clue that seems really valuable. You note that same-model transfer is generally early, as Cloud et al. would predict, whereas cross-model is generally late. This suggests different mechanisms in the two cases: gradient alignment for the same-model case, and, well, something else for cross-model.
5. For prompted subliminal learning, the generated dataset (in the limit) fully identifies the underlying prompt, and this goes a long way to explaining why the effects arise. This makes the results for generated sequences with no direct dependence on the prompt the most striking for me. That said, these seem to be results only for identical initializations, which makes them easier to rationalize, and I think the set-up is at least carrying over the teacher's bias for specific number tokens.
6. If we restrict to identical student and teacher initializations and focus on the prompted case, we have a principled reason to expect subliminal effects for OPD given @NathanHu12's work here: arxiv.org/abs/2609.16927 Nathan actually already conveyed to me that the insight behind his proof in Appendix A carries over to these objectives almost unchanged. However, in your settings (different models, fine-tuning), I am not sure how to get a toehold analytically.
Stella Biderman retweeted
can't believe it's actually working
What the actual fuck? Each of these would be a career defining accomplishment :O
Stella Biderman retweeted
Live now at board 211 :)
Excited to share that I'll be in Budapest for EMNLP 2026 (October 24–31) to present our paper, “Fractional Rotation, Full Potential? Investigating Performance and Convergence of Partial RoPE”!
This is joint work with Krishna Gummadi, Manish Gupta and @lasha_nlp!
Stella Biderman retweeted
Pangram is good, but has some well-known failure modes, like:
- A time you heard it didn't work
- A school essay you wrote years ago that it flagged as AI (you don't have a link or the essay text handy)
- A different AI detector got it wrong, and they're all the same, right?
Analysis by @KelseyTuoc is consistently ahead of the curve.
theargumentmag.com/p/the-cor…
I’m still working through it, but this work is incredibly cool and (unless there’s some kind of serious mistake) the lineage of work including this and “Why Larger Models Learn More” is exactly the kind of work that I think is necessary to develop a science of DL. I’m a huge fan
The wonderful “Don’t just Fix it in Post” paper identifies predicting memorization as a key open problem.
A progress report: we can now accurately predict verbatim memorization using just three well-understood variables. Experiments by Jing Huang, using the Hubble model suite:
ALT Dot plot addressing whether memorized sequences can be predicted without training the full model. Headline: “Largely yes, with Frequency and Recency doing most of the work.” Four rows show AUC-ROC (0.5 = chance, 1.0 = perfect) for a linear predictor of verbatim extraction, one mark per Hubble model (1B or 8B params; 100B or 500B tokens). All three features: 0.82–0.94. Frequency alone: 0.81–0.93. Recency alone: 0.82–0.90. Hardness alone: 0.51–0.55, barely above chance. In every row the 1B/500B model scores lowest and the 1B/100B model highest. Footnote: held-out strings, mean of 5 seeds; Frequency = copies of the string, Recency = steps since last copy, Hardness = NLL on models that never saw it.
for me to take a serious stab at solving when I got the bandwidth to do so (probably January tbh). Now I can spend that time on something else :)
I can’t wait for the full write-up!