I would claim that LLMs desperately need proprietary code in their training, before we see any big gains in quality.
There's some incredible source available code out there. Statistically, I think there's a LOT more not so great source available code out there, because the majority of output of seasoned/high skill developers is proprietary.
To me, a surprising portion of Claude 4.5 output definitely looks like student homework answers, because I think that's closer to the mean of the code population.
This is dead wrong: essentially the entirety of the huge gains in coding performance in the past year have come from RL, not from new sources of training data.
I echo the other commenters that proprietary code isn’t any better, plus it doesn’t matter because when you use LLMs to work on proprietary code, it has the code right there.
> it doesn’t matter because when you use LLMs to work on proprietary code, it has the code right there
The quality of the existing code base makes a huge difference. On a recent greenfield effort, Claude emitted an MVP that matched the design semantics, but the code was not up to standards. For example, it repeatedly loaded a large file into memory in different areas where it was needed (rather than loading once and passing a reference.)
However, after an early refactor, the subsequently generated code vastly improved. It honors the testing and performance paradigms, and it's so clean there's nothing for the linter to do.
Author attributes past year's degradation of code generation by LLMs to excessive use of new source of training data, namely, users' code generation conversations.
Yeah, this is a bullshit article. There is no such degradation, and it’s absurd to say so on the basis of a single problem which the author describes as technically impossible. It is a very contrived under-specified prompt.
And their “explanation” blaming the training data is just a guess on their part, one that I suspect is wrong. There is no argument given that that’s the actual cause of the observed phenomenon. It’s a just-so story: something that sounds like it could explain it but there’s no evidence it actually does.
My evidence is that RL is more relevant is that that’s what every single researcher and frontier lab employee I’ve heard speak about LLMs in the past year has said. I have never once heard any of them mention new sources of pretraining data, except maybe synthetic data they generate and verify themselves, which contradicts the author’s story because it’s not shitty code grabbed off the internet.
> Yeah, this is a bullshit article. There is no such degradation, and it’s absurd to say so on the basis of a single problem which the author describes as technically impossible. It is a very contrived under-specified prompt.
I see "No True Scotsman" argument above.
> My evidence is that RL is more relevant is that that’s what every single researcher and frontier lab employee I’ve heard speak about LLMs in the past year has said.
Reinforcement learning reinforces what is already in the LM, makes width of search path of possible correct answer narrower and wider search path in not-RL-tuned base models results in more correct answers [1].
> I have never once heard any of them mention new sources of pretraining data, except maybe synthetic data they generate and verify themselves, which contradicts the author’s story because it’s not shitty code grabbed off the internet.
The sources of training data already were the reasons for allegations, even leading to lawsuits. So I would suspect that no engineer from any LLM company would disclose anything on their sources of training data besides innocently sounding "synthetic data verified by ourselves."
From the days I have worked on blockchains, I am very skeptical about any company riding any hype. They face enormous competition and they will buy, borrow or steal their way to try to not go down even a little. So, until Anthropic opens the way they train their model so that we can reproduce their results, I will suspect they leaked test set into it and used users code generation conversation as new source of training data.
>>> It is a very contrived under-specified prompt.
No True Prompt can be such contrived and underspecified.
The article about degradation is a case study (single prompt), weakest of the studies in hierarchy of knowledge. Case studies are basis for further, more rigorous studies. And author took the time to test his assumptions and presented quite clear evidence that such degradation might be present and that we should investigate.
We have investigated. Millions of people are investigating all the time and finding that the coding capacity has improved dramatically over that time. A variety of very different benchmarks say the same. This one random guy’s stupid prompt says otherwise. Come on.
As far as I remember, article stated that he found same problematic behavior for many prompts, issued by him and his colleagues. The "stupid prompt" in article is for demonstration purposes.
But that’s not an argument, that’s just assertion, and it’s directly contradicted by all the more rigorous attempts to do the same thing through benchmarks (public and private).
Progress with RL is very interesting, but it's still too inefficient. Current models do OK on simple boring linear code. But they output complete nonsense when presented with some compact but mildly complex code, e.g. a NumPyro model with some nesting and einsums.
For this reason, to be truly useful, model outputs need to be verifiable. Formal verification with languages like Dafny , F*, or Isabelle might offer some solutions [1]. Otherwise, a gigantic software artifact such as a compiler is going to have a critical correctness bugs with far-fetched consequences if deployed in production.
Right now, I think treating a LLM like something different than a very useful information retrieval system with excellent semantic capabilities is not something I am comfortable with.
I firmly agree with your first sentence. I can just think about the various modders that have created patches and performance enhancing mods for games with budgets of tens to hundreds of millions of dollars.
But to give other devs and myself some grace, I do believe plenty of bad code can likely be explained by bad deadlines. After all, what's the Russian idiom? "There is nothing more permanent than the temporary."
yeah, but isn't the whole point of claude code to get people to provide preference data/telemetry data to anthropic (unless you opt out?). same w/ other providers.
i'm guessing most of the gains we've seen recently are post training rather than pretraining.
Yes, but you have the problem that a good portion of that is going to be AI generated.
But, I naively assume most orgs would opt out. I know some orgs have a proxy in place that will prevent certain proprietary code from passing through!
This makes me curious if, in the allow case, Anthropic is recording generated output, to maybe down-weight it if it's seen in the training data (or something similar)?
I'd bet, on average, the quality of proprietary code is worse than open-source code. There have been decades of accumulated slop generated by human agents with wildly varied skill levels, all vibe-coded by ruthless, incompetent corporate bosses.
It doesn’t matter what the average is though. If 1% of software is open source, there is significantly more closed source software out there and given normal skills distributions, that means there is at least as much high quality closed source software out there, if not significantly more. The trick is skipping the 95% of crap.
Not to mention, a team member is (surprise!) fired or let go, and no knowledge transfer exists. Womp, womp. Codebase just gets worse as the organization or team flails.
This is cool and actually demonstrates real utility. Using AI to take something that already exists and create it for a different library / framework / platform is cool. I'm sure there's a lot of training data in there for just this case.
But I wonder how it would fare given a language specification for a non-existent non-trivial language and build a compiler for that instead?
If you come up with a realistic language spec and wait maybe six months, by then it'll probably be approach being cheap enough that you could test the scenario yourself!
I see that as the point that all this is proving - most people, most of the time, are essentially reinventing the wheel at some scope and scale or another, so we’d all benefit from being able to find and copy each others’ homework more efficiently.
..A small thing, but it won't compile the RISCV version of hello.c if the source isn't installed on the machine it's running on.
It is standing on the shoulders of giants (all of the compilers of the past, built into it's training data... and the recent learnings about getting these agents to break up tasks) to get itself going. Still fairly impressive.
On a side-quest, I wonder where Anthropic is getting there power from. The whole energy debacle in the US at the moment probably means it made some CO2 in the process. Would be hard to avoid?
