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LessWrong (Curated & Popular)

LessWrong (Curated & Popular)

By: LessWrong
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© 2026 LessWrong (Curated & Popular) Hourly Philosophy Social Sciences
Episodes
  • "LLMs Are Starting To Noticeably Accelerate Our Work" by johnswentworth

    "LLMs Are Starting To Noticeably Accelerate Our Work" by johnswentworth
    Aug 12 2026
    About a year ago, David and I put up two bounty problems involving natural latents. I am now about 80% confident that both have been resolved, both within the past couple months. Both cases made heavy use of LLMs and Lean.

    The first to land was Grisha Pochuev's counterexample to the "Existence of a Deterministic Maximal Redund" conjecture. It's pretty readable, and I'm mostly convinced that it works. The original bounty post offered $500 for a proof or partial payout for a counterexample, with partial payout depending on how thoroughly the counterexample killed hope of any nearby variant of the conjecture. I think this counterexample is worth 300 dollars. Good job Grisha, and hopefully I can figure out a not-too-painful way to send you money.

    Meanwhile, for a couple months David has been cranking away on "secret project X", with the promise that he'd tell me what the project was if and when it bore fruit. Well, apparently it bore fruit; he now has a proof that existence of a stochastic natural latent implies existence of a deterministic natural latent, which was our other bounty problem. The proof is apparently "pretty gnarly", lots of cases, all LLM-coded in Lean. [...]

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    First published:
    August 11th, 2026

    Source:
    https://www.lesswrong.com/posts/7QvKqpGJwqXrQcMgx/llms-are-starting-to-noticeably-accelerate-our-work

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    4 mins
  • "There Will Come Soft Rains" by tanagrabeast

    "There Will Come Soft Rains" by tanagrabeast
    Aug 11 2026
    Today is August 4, 2026

    [Crossposted from AI StopWatch]

    In the living room the voice-clock sang, Tick-tock, seven o’clock, time to get up, time to get up, seven o’clock! as if it were afraid that nobody would.

    So begins Ray Bradbury's There Will Come Soft Rains, a short story that has haunted me for most of my life. Depicting the aftermath of nuclear war, it was first published in 1950. It takes place today.

    Literally:

    “Today is August 4, 2026,” said a second voice from the kitchen ceiling, “in the city of Allendale, California.” It repeated the date three more times for memory's sake. “Today is Mr. Featherstone's birthday. Today is the anniversary of Tilita's marriage. Insurance is payable, as are the water, gas, and light bills.”

    Was it narrative convenience or prophetic vision that drove Bradbury to depict the smart house of the future as gratuitously conspicuous in its competence, pointlessly reminding the owners of the year and their city of residence? There's something very Alexa-like about that — and about the janky brittleness evident in the system as it prepares breakfast for a family that won’t be eating and opens the garage door for a father who [...]

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    First published:
    August 4th, 2026

    Source:
    https://www.lesswrong.com/posts/aowxE8xZ8xkhRCn9r/there-will-come-soft-rains-1

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    6 mins
  • "Four LLM loss functions → four flavors of LLM misalignment" by Steven Byrnes

    "Four LLM loss functions → four flavors of LLM misalignment" by Steven Byrnes
    Aug 11 2026
    It seems to me that, for every loss function that we use to train LLMs, we get a very distinct flavor of LLM misalignment. Here's the summary table, and then we’ll go through the rows separately.

    Training stage

    Loss function

    Flavor of misalignment

    Famous examples

    Pretraining & SFT

    Imitative learning (next-token prediction)

    “Seven deadly sins” misalignment

    Bing-Sydney, “Emergent misalignment”

    RLHF & DPO

    Human approval

    “Glazing” misalignment

    GPT-4o

    RLVR

    Automatic verifier

    “Literal genie” misalignment

    HuggingFace hacking

    RLAIF

    Approval from another LLM

    “Trickster” misalignment

    “Current AIs seem pretty misaligned to me”

    Warning: I’m not an LLM power-user myself, but rather relying on reports I’ve read. Also, I don’t consider LLM alignment to be my primary area of expertise. I’m open to feedback!

    1. Imitative learning → “seven deadly sins” misalignment

    Training stage

    Loss function

    Misaligned behavior

    Pretraining, SFT

    Imitative learning (next-token prediction)

    Any and all of the vices of humanity

    In imitative learning, the LLM tries to predict what the next token of text will be. Then those predictions magically turn into its outputs. See my earlier discussion: “LLM pretraining magically transmutes observations into behavior, in a way that is profoundly disanalogous to how brains work”.

    This leads to LLM behavior [...]

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    Outline:

    (00:55) 1. Imitative learning → "seven deadly sins" misalignment

    (04:24) 2. Human approval → "glazing" misalignment

    (06:35) 3. Automatic verifiers → "literal genie" misalignment

    (08:05) 4. LLM judges → "trickster" misalignment

    (12:06) Afterword

    The original text contained 1 footnote which was omitted from this narration.

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    First published:
    August 10th, 2026

    Source:
    https://www.lesswrong.com/posts/GRmvZsHXH4vaijPMv/four-llm-loss-functions-four-flavors-of-llm-misalignment

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    Narrated by TYPE III AUDIO.
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    13 mins
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