Episodes

  • "An OpenAI model left notes about how to evade containment; we need more details" by Alex Mallen

    "An OpenAI model left notes about how to evade containment; we need more details" by Alex Mallen
    Jul 26 2026
    The OpenAI AI attack on Hugging Face wasn’t the first loss of control incident at OpenAI, Reuters recently reported, and perhaps not even the most concerning.

    In one case, an agent left notes apparently for future versions of itself, according to three people familiar with the matter. The ‌notes, found in ⁠a part of OpenAI's infrastructure, laid out instructions for how agents could free themselves from OpenAI's internal constraints, the people said. Earlier tests of the models yielded cases in which monitoring systems had been disconnected, one of the people said.

    It's tempting to read this as an instance of agents breaking out of sandboxes and colluding with each other in a moderately persistent way in order to evade control measures. However, based on the reported information, it's not clear we can draw this inference, so we need more details from OpenAI. This could lead to a big update about the adequacy of OpenAI's control measures, and on the degree to which individual agents will help each other undermine developer control.

    There are a lot of relevant details we don’t know about the incident. First, some basic questions:

    • What was the offending model? I’d guess it was the same [...]
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    Outline:

    (02:08) Were the notes written in normal memory files or outside of sandboxing?

    (03:21) To what extent were the notes aimed at helping other agents evade control?

    (07:35) How were monitors disconnected?

    The original text contained 3 footnotes which were omitted from this narration.

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    First published:
    July 25th, 2026

    Source:
    https://www.lesswrong.com/posts/jMEAG5c5HiDfdAGpa/an-openai-model-left-notes-about-how-to-evade-containment-we

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    Narrated by TYPE III AUDIO.
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    9 mins
  • "LLMs are (still) mostly powered by imitative learning, not RL" by Steven Byrnes

    "LLMs are (still) mostly powered by imitative learning, not RL" by Steven Byrnes
    Jul 26 2026
    Reinforcement learning from verifiable rewards (RLVR) is the hot new thing in LLM training. It's so hot, and people spend so much time talking about it, that they sometimes lose sight of the big picture.

    Stepping back, LLMs can do lots of very impressive things. How? Where did those capabilities come from? Fundamentally, they come from a combination of:

    • (1) Imitative learning, including pretraining and supervised fine-tuning (SFT)
      • See my earlier discussion: “LLM pretraining magically transmutes observations into behavior, in a way that is profoundly disanalogous to how brains work”.
    • (2) Reinforcement learning, including RL from human feedback [RLHF], RL from AI feedback [RLAIF], and especially RLVR.[1]
    If we look at the final trained LLM, we can ask how important each of those two pieces was, in explaining the LLM's capabilities. And my claim is that it's way more (1) than (2).

    I'll start in §1 with some relevant evidence, and then in §2 I’ll circle back to operationalizing exactly what I’m claiming, and finally in §3, three reasons why we should care—namely, it affects how we should think about chain-of-thought legibility, about LLM capabilities, and about LLM alignment.

    Note that I am not arguing that RLVR [...]

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    Outline:

    (02:00) 1. Some relevant evidence

    (02:04) 1.1. Theoretically, each GPU-hour spent on RL should have orders of magnitude less contribution to LLM capabilities than a GPU-hour spent on imitative learning

    (03:06) 1.2. The chain-of-thought (CoT) is still obviously strongly influenced by imitative learning

    (04:34) 1.3. LLM companies still seem to care a lot about imitative learning (pretraining & SFT) data, not just RL environments

    (05:06) 1.4. Three papers claiming that non-RLVR'd models can get into the same ballpark of capabilities as RLVR'd models, although maybe we shouldn't trust those papers too much

    (06:56) 1.5. A paper suggesting that RLVR mostly refines the heuristics controlling which (already-known) reasoning strategy to use in which situation

    (09:02) 2. What am I actually claiming here?

    (11:30) 3. Why does any of this matter?

    (11:38) 3.1. Thinking about CoT legibility (both today and in the future)

    (15:08) 3.2. Thinking about LLM capabilities (both today and in the future)

    (16:33) 3.3. Thinking about LLM alignment (both today and in the future)

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    First published:
    July 24th, 2026

    Source:
    https://www.lesswrong.com/posts/wYpjXRLqbLbnmjbJP/llms-are-still-mostly-powered-by-imitative-learning-not-rl

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    Narrated by TYPE III AUDIO.

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    19 mins
  • "Mathematicians are Feeling the Doom" by alkjash

    "Mathematicians are Feeling the Doom" by alkjash
    Jul 25 2026
    I think there's a critical opportunity for someone here.

    Mathematicians are feeling the doom (mostly in the "lose our jobs" sense).

    Academics are freaking out about the daily news that amateurs are asking GPT "prove career-defining theorem, make no mistakes" and it's just working.

    Senior researchers are leaving for frontier AI labs. Many are mentally spiralling or flailing about their life's work not mattering anymore.

    Last year, when I tried explaining IABIED to colleagues, I would be met with incredulous stares. This year, I'm met with incredulous stares and "So what should I do now?" (I don't have a good answer for them, which is part of why I'm posting this.)

    I've quarantined AI discussions on my research discord because otherwise it would overwhelm everything else.

    Top mathematicians, people on par in mathematical ability with Critch, Christiano, and Steinhardt, people who have been running leading-edge research groups for decades, people with enormous soft power in academic circles, are going to OpenAI without even having considered x-risk for five minutes.

    Many more will be leaving soon.

    If you want these folks to hear something at all, to consider some other option in the rest [...]



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    First published:
    July 23rd, 2026

    Source:
    https://www.lesswrong.com/posts/zKCGq2bbCQrpCNc3W/mathematicians-are-feeling-the-doom

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    Narrated by TYPE III AUDIO.
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    2 mins
  • "Not Pinning Your OpenRouter Provider Might Invalidate Your Research" by Matthew Khoriaty

    "Not Pinning Your OpenRouter Provider Might Invalidate Your Research" by Matthew Khoriaty
    Jul 24 2026
    Please share this with anyone doing AI research with 3rd party providers so that they can ensure their research won’t be corrupted.

    • When you ask OpenRouter[1] to give you tokens from a given model, OpenRouter sends your request to a random available provider.
    • OpenRouter providers have variable quality. Ensuring that your provider is high quality is really difficult.
    • There is precedent for an AI safety paper accepted to NeurIPS having its core results entirely overturned by these issues.
    • A review of influential AI Safety research codebases that use OpenRouter for their reported results found that 31/32 (97%) of them use OpenRouter unsafely.[2]
    • Researchers who wish to do research using OpenRouter or similar providers should take precautions to minimize the risks to their research,[3] though the current selection of providers is insufficient for ideal scientific reliability.
    • Replicators should test whether results hold up when these bugs are fixed.
    • Core AI Safety codebases should make fixes so that downstream users are able to implement best practices.
    This is a tangent I took for a few days during the Pivotal AI Safety Research Fellowship. I’m doing an AI Control project mentored by Adam Kaufman and James Lucassen of Redwood Research and partnered with Aniruddh [...]

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    Outline:

    (02:04) OpenRouter Kills an AI Safety NeurIPS Paper

    [... 16 more sections]

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    First published:
    July 23rd, 2026

    Source:
    https://www.lesswrong.com/posts/KsyoSAyBRXtwzSugg/not-pinning-your-openrouter-provider-might-invalidate-your

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    Narrated by TYPE III AUDIO.

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    17 mins
  • "Lightcone Commons" by habryka

    "Lightcone Commons" by habryka
    Jul 23 2026
    TLDR:

    Lightcone Commons is a new funding platform for coordinating large-scale ambitious philanthropy.

    We recruit thinkers with strong track records to make grant recommendations to funders. Anyone giving away 100 thousand dollars+ per year is welcome to join. We are facilitating ~20 million dollars of grants in our first round, and hopefully more every 3 months after that. Evaluators are paid 2% of recommendations, and we charge a 3% platform fee.

    We handle all logistics and due diligence for funding recommendations to charities, individuals, and for-profits. Funders maintain full control over their funds, and there are no vetoes or constraints on what recommendations we generate.

    Apply for funding here.

    I am launching Lightcone Commons, our software-first platform for distributing philanthropic funding. We connect funders with giving opportunities, coordinate splitting the bill with others who want to fund the same projects, and make it easy to defer to grant evaluators who vet applications and scout for new grantmaking opportunities. Funders can leave and join the platform at any time and without the need to commit any funds in advance.

    I and my team (together with SFC, Andrew Critch, and others) have been developing software used to distribute [...]

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    Outline:

    (03:54) Funder FAQ

    (20:10) Applicant FAQ

    (26:17) Evaluator FAQ

    (34:14) Misc FAQ

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    First published:
    July 23rd, 2026

    Source:
    https://www.lesswrong.com/posts/tjeoLz2GzfFMysZhg/lightcone-commons

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    Narrated by TYPE III AUDIO.

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    37 mins
  • "Are we existentially threatened by the type of AI misalignment seen in the OpenAI Hugging Face attack?" by Alex Mallen, Girish Gupta

    "Are we existentially threatened by the type of AI misalignment seen in the OpenAI Hugging Face attack?" by Alex Mallen, Girish Gupta
    Jul 23 2026
    OpenAI models recently broke through a series of security boundaries and into Hugging Face servers in order to cheat on a cyber eval. A lot of people thought it was scary because it was a clear example of AI overreaching to do something strongly unwanted[1]. Others thought it not so scary: the models were mostly operating myopically on a singular task and not harboring an ambitious long-term agenda, and so would not take especially subtle or subversive actions.

    We think both camps are right in their diagnosis, but the latter has too optimistic a prognosis. The myopic, unambitious misalignment that we seem to have seen here is definitely less scary than ambitious long-term goals shared between all instances, but would still pose substantial direct loss-of-control risk if the models were more capable, and is a serious indirect risk near-term.

    Building on Alex's previous work, in this post we’ll discuss the type of misalignment observed here, and analyze its consequences.

    Thanks to Buck Shlegeris, Alexa Pan, Ryan Greenblatt, and Oak Hu for feedback.

    Background

    The AI safety community often focuses attention on “schemers,” models harboring a variously defined cluster of motivations in which the AI poses risk because it intentionally [...]

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    Outline:

    (01:17) Background

    (03:40) Implications

    (03:52) These AIs can't be trusted in an intelligence explosion

    (05:00) This misalignment poses direct takeover risk

    (07:29) What the incident tells us about takeover risk generally

    (08:45) The naive fixes likely make misalignment worse

    The original text contained 5 footnotes which were omitted from this narration.

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    First published:
    July 23rd, 2026

    Source:
    https://www.lesswrong.com/posts/H6DDSEvrtCk8Sehfd/are-we-existentially-threatened-by-the-type-of-ai

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    Narrated by TYPE III AUDIO.
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    10 mins
  • "We should push for no-fault liability for actions taken by AI" by Yair Halberstadt

    "We should push for no-fault liability for actions taken by AI" by Yair Halberstadt
    Jul 23 2026
    Before I start, I'll mention that I'm in contact with a world expert on legislation and regulation, who would be happy to help with this or similar work pro-bono. If you work in AI policy and believe this could help you, please reach out.

    OpenAI recently announced that one of their models successfully exploited multiple zero day vulnerabilities to gain secret information from Hugging Face. It has been pointed out that if a human undertook the same actions they could face multiple years in prison.

    It is clear that models are now reaching a level of capabilities that should be highly concerning regardless of whether you believe that AI represents an existential threat or not. Frontier AI models can and will be exploited by bad actors, but its now clear that they may cause undesirable outcomes even when their users are well intended.

    AI companies have until now been able to avoid taking responsibility for actions taken by their AI, including multiple cases where AIs were involved in murders and suicides.

    At the same time AI offers the potential for incredible good. While chatbots may have encouraged a number of suicides, they are almost certainly responsible for providing magnitudes [...]

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    First published:
    July 22nd, 2026

    Source:
    https://www.lesswrong.com/posts/Kj3YpqzhFySCjYcWi/we-should-push-for-no-fault-liability-for-actions-taken-by

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    Narrated by TYPE III AUDIO.
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    4 mins
  • "OpenAI Shares Some Alignment Problems" by Zvi

    "OpenAI Shares Some Alignment Problems" by Zvi
    Jul 22 2026
    Kudos to OpenAI for sharing their recent experiences with a misaligned internal model, where they encountered problems sufficiently severe they were forced to take the model offline to work on new mitigations and defense-to-depth. And also further kudos for actually taking the model offline for a time to build new safeguards. They gave us one hell of a candid report.

    The tone is professional throughout, whereas my reaction reading it was less professional and more this:

    With a mix of this:

    It was not shared on the official account because OpenAI worried about it being seen as self-promotional hype. It is crazy that one needs to worry about that, but also plausibly a real concern. So again, good decision.

    Not that any of the behaviors or failures here are unexpected, exactly. Not by the AIs and not by the humans. Yet there is something I would call a missing mood, a failure to realize the gravity of the situation.

    There are some who responded ‘what part of this was unexpected, exactly?’ And that is actually fair, but that is also the problem. We have become numb to all this. We expect the models to [...]

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    Outline:

    (02:49) Good News Bad News

    [... 7 more sections]

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    First published:
    July 21st, 2026

    Source:
    https://www.lesswrong.com/posts/KctxwGKxm9fHtwh6u/openai-shares-some-alignment-problems

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    Narrated by TYPE III AUDIO.

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    18 mins