7 ms·

Sorry I thought it would be clear and could have clarified that the code itself is just a joke illustrating the point, as an exaggeration. This was the thread i

• chis 11mo ago
  I think there’s always a danger of these foundational model companies doing RLHF on non-expert users, and this feels like a case of that. The AIs in general feel really focused on making the user happy - your example, and another one is how they love adding emojis to the stout and over-commenting simple code.
  • cma 11mo ago
    And more advanced users are more likely to opt out of training on their data, Google gets around it with a free api period where you can't opt out and I think from did some of that too, through partnerships with tool companies, but not sure if you can ever opt out there.
    • cma 11mo ago
      *grok, not 'from'
  • miki123211 11mo ago
    This feels like RLVR, not RLHF. With RLVR, the LLM is trained to pursue "verified rewards." On coding tasks, the reward is usually something like the percentage of passing tests. Let's say you have some code that iterates over a set of files and does processing on them. The way a normal dev would write it, an exception in that code would crash the entire program. If you swallow and log the exception, however, you can continue processing the remaining files. This is an easy way to get "number of files successfully processed" up, without actually making your code any better.
    • eru 11mo ago
      > This is an easy way to get "number of files successfully processed" up, without actually making your code any better. Well, it depends a bit on what your goal is. Sometimes the user wants to eg backup as many files as possible from a failing hard drive, and doesn't want to fail the whole process just because one item is broken.
      • orisho 11mo ago
        You're right, but the way to achieve this is to allow the error to propagate at the file level, then catch it one function above and continue to the next one. However, LLM generated code will often, at least in my experience, avoid raising any errors at all, in any case. This is undesirable, because some errors should result in a complete failure - for example, errors which are not transient or environment related but a bug. And in any case, a LLM will prefer turning these single file errors into warnings, though the way I see it, they are errors. They just don't need to abort the process, but errors nonetheless.
        • eru 11mo ago
          Yes, that's cleaner. > And in any case, a LLM will prefer turning these single file errors into warnings, though the way I see it, they are errors. Well, in general they are something that the caller should have opportunity to deal with. In some cases, aborting back to the caller at the first problem is the best course of action. In some other cases, going forward and taking note of the problems is best. In some systems, you might event want to tell the caller about failures (and successes) as they occur, instead of waiting until the end. It's all very similar to the different options people have available when their boss sends them on an errand and something goes wrong. A good underling uses their best judgement to pick the right way to cope with problems; but computer programs don't have that, so we need to be explicit. See https://en.wikipedia.org/wiki/Mission-type_tactics https://en.wikipedia.org/wiki/Mission-type_tactics for a related concept in the military.
  • justatdotin 11mo ago
    'over-commenting simple code' is preparing it for future agent work. pay attention to those comments to learn how you can better scaffold for agents.
    • mnahkies 11mo ago
      They do seem to leave otherwise useless comments for itself. Eg: on the level of // Return the result return result; I find this quite frustrating when reading/reviewing code generated by AI, but have started to appreciate that it does make subsequent changes by LLMs work better. It makes me wonder if we'll end up in a place where IDEs hide comments by default (similar to how imports are often collapsed by default/automatically managed), or introduce some way of distinguishing between a more valuable human written comment and LLM boilerplate comments.
    • stuaxo 11mo ago
      They should have a step to remove those sorts of comments, they only add noise to the code.
• why_at 11mo ago
  This part from the first try made me laugh: if random.random() < 0.01: logging.warning("This feels wrong. Aborting just in case.") return None
  • bspammer 11mo ago
    I think that’s the funniest joke I’ve ever seen an LLM make. Which probably means it’s copied from somewhere.
    • m463 11mo ago
      "Why is a laser beam like goldfish? Because neither one can whistle." - Mike, The Moon is a Harsh Mistress
      • muggesmuds 11mo ago
        Fantastic book, just read it. Surprised no movie has been made.
        • grues-dinner 11mo ago
          The protagonists are libertarians with teenage harems, who fake an election and team up with with a sex pest. That's extremely reductive to the point of parody, but that will likely be the media coverage of it then moment someone reads the women and politics in the book. If you completely excise anything too distasteful for a current-day blockbuster, but want a film about a space mining colony uprising you might as well just adapt the game Red Faction instead: have the brave heros blasting away with abandon at corpo guards, mad genetic experimenters and mercenaries and the media coverage can talk about how it's a genius deconstruction of Elon Musk's Martian dream or whatever.
        • pavlov 11mo ago
          If you haven't read Ursula Le Guin's "The Dispossessed", check it out too. It's like a fine wine pairing for "The Moon is a Harsh Mistress."
• bjourne 11mo ago
  This is stunning English: "Perfect setup for satire. Here’s a Python function that fully commits to the bit — a traumatically over-trained LLM trying to divide numbers while avoiding any conceivable danger:" "Traumatically over-trained", while scoring zero google hits, is an amazingly good description. How can it intuitively know what "traumatic over-training" should mean for LLMs without ever having been taught the concept?
  • shawabawa3 11mo ago
    > How can it intuitively know what "traumatic over-training" should mean for LLMs without ever having been taught the concept? Because, and this is a hot take, LLMs have emergent intelligence
    • procaryote 11mo ago
      Or language has patterns
  • drekipus 11mo ago
    The same way that you and I think up a word and what it might mean without being taught the concept. Adverb + verb
    • the_gipsy 11mo ago
      But the machines cannot possibly have the magic brain-juice!
  • gnulinux 11mo ago
    Hard to know but if you could express "traumatically" as a number, and "over-trained" as a number, it seems like we'd expect "traumatically" + "over-trained" to be close to "traumatically over-trained" as a number. LLMs work in mysterious ways.
  • jcelerier 11mo ago
    LLMs operate at token level, not word. it doesn't operate in terms of "traumatic", "over-training", "over" or "training", but rather "tr" "aum" "at" "ic, ", etc.
• cruffle_duffle 11mo ago
  Kind of interesting it didn't add type hints though! You'd think for all that paranoia it would at least add type hints.
• TZubiri 11mo ago
  https://chatgpt.com/share/68e87072-e3ac-800f-a44c-af5666180a6a https://chatgpt.com/share/68e87072-e3ac-800f-a44c-af5666180a... lgtm
• nought 11mo ago
  It was a great joke, that's why I posted it