Posted by Anon84 13 hours ago
> Code interleaves fork positions, where several continuations are genuinely plausible and may correspond to different solution approaches, with lock positions, where syntax and semantics leave little ambiguity but a low-probability distractor tail still remains… The best global decoding setting is therefore necessarily a compromise; we call this tension the precision-exploration conflict.
In other words, just like us, the model needs to shift from "exploration" in "fork" mode (divergent thinking to produce a creative solution) to "precision" in "lock" mode (producing syntactically correct code).
What this paper shows is that their simple technique (SSD) can improve the ranking of optimal tokens in both lock and fork positions, meaning the model is more likely to explore when it should be exploring, and more likely to be precise when it needs to be.
I love that we're still learning the emergent properties of LLMs!
TBH, this is (very much my opinion btw) the least surprising thing. LLMs (and especially their emergent properties) are still black boxes. Humans have been studying the human brain for millenia, and we are barely better at predicting how humans work (or for eg to what extent free will is a thing). Hell, emergent properties of traffic was not understood or properly given attention to, even when a researcher, as a driver, knows what a driver does. Right now, on the front page, is this post:
> 14. Claude Code Found a Linux Vulnerability Hidden for 23 Years (mtlynch.io)
So it's pretty cool we're learning new things about LLMs, sure, but it's barely surprising that we're still learning it.
(Sorry, mini grumpy man rant over. I just wish we knew more of the world but I know that's not realistic.)
I dare say that in some ways, we understand LLMs better than humans, or at least the interpretability tools are now superior. Awkward place to be, but an interesting one.
Are you surprised we understand them better than brains?
Very, monsieur Laplace.
That's a bit of an overstatement.
The entire field of ML is aimed at problems where deterministic code would work just fine, but the amount of cases it would need to cover is too large to be practical (note, this has nothing to do with the impossibility of its design) AND there's a sufficient corpus of data that allows plausible enough models to be trained. So we accept the occasionally questionable precision of ML models over the huge time and money costs of engineering these kinds of systems the traditional way. LLMs are no different.
What you are saying is fantasy nonsense.
> but the amount of cases it would need to cover is too large to be practical (note, this has nothing to do with the impossibility of its design)
So it doesn't work.
The GP said, "I'm a psychiatry resident".
The entire industry is propped up by misinformed people burping up the CEO farts they are sucking.
You would be sorely mistaken to think I'm utterly uninformed about LLM-research, even if I would never dare to claim to be a domain expert.
We have tons of low-hanging fruits across all fields of science and engineering to be picked, in form of different ways to apply and chain the models we have, different ways to interact with them, etc. - enough to fuel a good decade of continued progress in everything.
Much as Diogenes mocked Platos definition of a man with a plucked chicken, LLM's revealed what "real" ai would require: contigous learning. That isnt to diminish the power of LLM's (the are useful) but that limitation is a fairly hard one to over come if true AGI is your goal.
From what I understand, a living neural network learns several orders of magnitude more efficiently than an artificial one.
I'm not sure where that difference comes from. But my brain probably isn't doing back propagation, it's probably doing something very different.
(eg different kinds of learning for long-term memory, short-term memory, languages, faces and reflexes.)
The intersection of what with physics?
Sir Roger Penrose, on quantum consciousness (and there is some regret on his part here) -- OR -- Jacob Barandes for a much more current thinking on this sort of intersectional exploratory thinking.
> The earliest reference to the brain occurs in the Edwin Smith Surgical Papyrus, written in the 17th century BC.
I was actually thinking of ancient greeks when writing my comment, but I suppose Egyptians have even older records than them.
I think that with grammar-aware sampling / constrained decoding [0][1] it is possible to sometimes skip calling the model altogether if only one token is allowed by grammar and just insert it, but I don't think that any of the current, widely used combinations of models/harnesses use it. And it only skips inference in rare edge cases.
I wonder if there is a more general solution that can make models spend more compute on making important choices, while making generation of the "obvious" tokens cheaper and faster.
[0] https://github.com/ggml-org/llama.cpp/blob/master/grammars/R...
[1] https://developers.redhat.com/articles/2025/06/03/structured...
Making coding agents spit out syntactically correct code token by token is like asking a human to code on a whiteboard.
We kinda have a little bit of it with some coding harnesses giving model access to LSP, but I think that we can insert this knowledge on a lower level if we find a clever way to somehow utilize it during sampling.
I think that there is a lot of low hanging fruit in this area.
And in general, I think that people try to use LLMs too much to solve problems that can be easily solved by cheaper (computationally), and, more importantly deterministic tools.
For example, back in the day when LLM-assisted coding just became a thing people very often complained about models generating syntactically incorrect code and inventing non-existent library methods.
Well, I, an experienced human programmer, probably would also be making syntax mistakes and inventing non-existent methods if you stripped me of my tools and made me write code in a bare text editor without syntax highlighting.
Thankfully, my IDE would autocomplete real syntax and actually existing library methods for me and immediately give me feedback if I make a mistake anyway. And all of it is achieved using reliable deterministic code without the inherent issues of statistical models.
I think that it is really inefficient to reach for an expensive and unreliable tool when a cheap and reliable tool will do.
1. code
2. syntax check / build / format / lint (details language dependent)
3. test
and they can hop between 1 and 2 however many times they want.
I do think there is some merit in a tool that dumps all namespaces and reachable symbols so the agent can do its own autocomplete without a round-trip.
As a human coder you don’t summon intellisense. It’s just popped up into your visual field as extra input - contextual cues.
You could force intellisense state into the context vector the LLM receives.
i once asked an LLM if it could ingest code from an interactive session more easily if it were in appropriately-typed markdown fences and it said absolutely yes, and that the syntax highlighting fed to it that way helps it immensely. i was downright shocked that syntax highlighting was anything more than noise for them.
I think speculative decoding count as a (perhaps crude) way implementing this?
There's a lot of work going on in various streams towards making it possible to vary compute per-token, dynamically, e.g. universal transformers. Maybe one day it'll work well enough to beat conventional techniques.
I got unstuck by randomizing the field order for each row?!? At training, and now I'm thinking I should do the same at inference time...
> This is probably due to the way larger numbers are tokenised, as big numbers can be split up into arbitrary forms. Take the integer 123456789. A BPE tokenizer (e.g., GPT-style) might split it like: ‘123’ ‘456’ ‘789’ or: ‘12’ ‘345’ ‘67’ ‘89’
One of the craziest LLM hacks that doesn't get love is https://polymathic-ai.org/blog/xval/
xVal basically says "tokenizing numbers is hard: what if instead of outputting tokens that combine to represent numbers, we just output the numbers themselves, right there in the output embedding?"
It works! Imagine you're discussing math with someone. Instead of saying "x is twenty five, which is large" in words, you'd say "x is", then switch to making a whistling noise in which the pitch of your whistle, in its position within your output frequency range, communicated the concept of 25.00 +/- epsilon. Then you'd resume speech and say "which is large".
I think the sentiment is that today's models are big and well-trained enough that receiving and delivering quantities as tokens representing numbers doesn't hurt capabilities much, but I'm still fascinated by xVal's much more elegant approach.
Completely artistic creation, creating something that does not exist and that cannot produce things out of itself, means that locking can be more diffuse, not as settled.
I think you are implying a reverse causation. They used a metaphor from us.
In nemotron the high perplexity solutions are selected for RL, in VLM training a few people are looking at the entropy distributions of the training set, etc
>I love that we're still learning the emergent properties of LLMs!
There are tons of low-hanging fruits there.
"Simple Self-Distillation". We had an acronym for Solid-State Drive. Don't know about that technique but the naming sure sound.. Simple?
That already looks like Sonnet 3x and 4 level capabilities to me where the model in question (Gemma 4) set ups whole python project with a UI and installs python libraries using uv etc.
Add this Simple Self Distillation to the picture and by 2028 I see cheaper coding model providers with much more generous usage limits in the future and power users would be mostly running their own models anyway.
Anyone using these models as "non-deterministic transpilers" from natural language to code (experienced engineers who can write code themselves) would probably not be paying to any AI providers.
Right now it feels like hammering a house onto a nail instead of the other way around.
LLMs have something that's not entirely unlike the "g factor" in humans - a broad "capability base" that spans domains. The best of the best "coding LLMs" need both good "in-domain training" for coding specifically and a high "capability base". And a lot of where that "base" comes from is: model size and the scale of data and compute used in pre-training.
Reducing the model scale and pruning the training data would result in a model with a lower "base". It would also hurt in-domain performance - because capabilities generalize and transfer, and pruning C code from the training data would "unteach" the model things that also apply to code in PHP.
Thus, the pursuit of "narrow specialist LLMs" is misguided, as a rule.
Unless you have a well defined set bar that, once cleared, makes the task solved, and there is no risk of scope adjustment, no benefit from any future capability improvements above that bar, and enough load to justify the engineering costs of training a purpose-specific model? A "strong generalist" LLM is typically a better bet than a "narrow specialist".
In practice, this is an incredibly rare set of conditions to be met.
There are hardware-based limitations in the size of LLMs you can feasibly train and serve, which imposes a limit in the amount of information you can pack into a single model's weights, and the amount of compute per second you can get out of that model at inference-time.
My company has been working on this specifically because even now most researchers don't seem to really understand that this is just as much an economics and knowledge problem (cf Hayek) as it is "intelligence"
It is much more efficient to strategically delegate specialized tasks, or ones that require a lot of tokens but not a lot of intelligence, to models that can be served more cheap. This is one of the things that Claude Code does very well. It's also the basis for MOE and some similar architectures with a smarter router model serving as a common base between the experts.
...with a fair amount of supervision, while frontier models would be running circles around them using project-specific memory and on-demand training (or whatever we would have by then).
If you're building something groundbreaking and new, the advantage will be slim to none.
https://ai.meta.com/research/publications/adaptive-decoding-...
(Not fine tuning, but interesting none the less. If a model can so easily find a more elegant solution, why didn't it pick that in the first place?)
We really need to develop better tools to understand what's happening inside these NNs. Working with high-D spaces is not something we're good at, and we're basically throwing stuff at it and seeing if it sticks.
I suppose we just don't have a deeper underlying theory to lean on and help us 'design' anything.
I often find, if I've got a complicated solution, it’s because I haven’t fully examined the problem.
So no, they are not fine-tuning a general purpose model to produce "valid benchmark code results."