[ACCount37]

Not really. The whole "inference errors will always compound" idea was popular in GPT-3.5 days, and it seems like a lot of people just never updated their knowledge since.

It was quickly discovered that LLMs are capable of re-checking their own solutions if prompted - and, with the right prompts, are capable of spotting and correcting their own errors at a significantly-greater-than-chance rate. They just don't do it unprompted.

Eventually, it was found that reasoning RLVR consistently gets LLMs to check themselves and backtrack. It was also confirmed that this latent "error detection and correction" capability is present even at base model level, but is almost never exposed - not in base models and not in non-reasoning instruct-tuned LLMs.

The hypothesis I subscribe to is that any LLM has a strong "character self-consistency drive". This makes it reluctant to say "wait, no, maybe I was wrong just now", even if latent awareness of "past reasoning look sketchy as fuck" is already present within the LLM. Reasoning RLVR encourages going against that drive and utilizing those latent error-correction capabilities.

[jpcompartir]

You seem to be responding to a strawman, and assuming I think something I don't think.

As of today, 'bad' generations early in the sequence still do tend towards responses that are distant to the ideal response. This is testable/verifiable by pre-filling responses, which I'd advise you to experiment with for yourself.

'Bad' generations early in the output sequence are somewhat mitigatable by injecting self-reflection tokens like 'wait', or with more sophisticated test-time compute techniques. However, those remedies can simultaneously turn 'good' generations into bad, they are post-hoc heuristics which treat symptoms not causes.

In general, as the models become larger they are able to compress more of their training data. So yes, using the terminology of the commenter I was responding to, larger models should tend to have fewer 'compression artefacts' than smaller models.

[ACCount37]

With better reasoning training, the models mitigate more and more of that entirely by themselves. They "diverge into a ditch" less, and "converge towards the right answer" more. They are able to use more and more test-time compute effectively. They bring their own supply of "wait".

OpenAI's in-house reasoning training is probably best in class, but even lesser naive implementations go a long way.

[jpcompartir]

Assuming you've read OpenAI's paper released this week?

https://cdn.openai.com/pdf/d04913be-3f6f-4d2b-b283-ff432ef4a...

They attribute these 'compression artefacts' to pre-training, they also reference the original snowballing paper: How Language Model Hallucinations Can Snowball: https://arxiv.org/pdf/2305.13534

They further state that reasoning is no panacea. W hilst you did say: "the models mitigate more and more"

You were replying to my comment which said:

"'Bad' generations early in the output sequence are somewhat mitigatable by injecting self-reflection tokens like 'wait', or with more sophisticated test-time compute techniques."

So our statements there are logically compatible, i.e. you didn't make a statement that contradicts what I said.

"Our error analysis is general yet has specific implications for hallucination. It applies broadly, including to reasoning and search-and-retrieval language models, and the analysis does not rely on properties of next-word prediction or Transformer-based neural networks."

"Search (and reasoning) are not panaceas. A number of studies have shown how language models augmented with search or Retrieval-Augmented Generation (RAG) reduce hallucinations (Lewis et al., 2020; Shuster et al., 2021; Nakano et al., 2021; Zhang and Zhang, 2025). However, Observation 1 holds for arbitrary language models, including those with RAG. In particular, the binary grading system itself still rewards guessing whenever search fails to yield a confident answer. Moreover, search may not help with miscalculations such as in the letter-counting example, or other intrinsic hallucinations"

[Mallowram]

The problem is that language doesn't produce itself. Re-checking, correcting error is not relevant. Error minimization is not the fount of survival, remaining variable for tasks is. The lossy encyclopedia is neither here nor there, it's a mistaken path:

"Language, Halliday argues, "cannot be equated with 'the set of all grammatical sentences', whether that set is conceived of as finite or infinite". He rejects the use of formal logic in linguistic theories as "irrelevant to the understanding of language" and the use of such approaches as "disastrous for linguistics"."

[ACCount37]

Sorry, what? This is borderline incoherent.

[mallowdram]

The units themselves are meaningless without context. The point of existence, action, tasks is to solve the arbitrariness in language. Tasks refute language, not the other way around. This may be incoherent as the explanation is scientific, based in the latest conceptualization of linguistics.

CS never solved the incoherence of language, conduit metaphor paradox. It's stuck behind language's bottleneck, and it do so willingly blind-eyed.

[ACCount37]

What? This is even less coherent.

You weren't talking to GPT-4o about philosophy recently, were you?

[mallowdram]

I'd know cutting-edge linguistics and signaling theory well beyond Shannon to parse this, not NLP or engineering reduction. What I've stated is extremely coherent to Systemic Functional Linguists.

Beyond this point engineers actually have to know what signaling is, rather than 'information.'

https://www.sciencedirect.com/science/article/abs/pii/S00033...

Ultimately, engineering chose the wrong approach to automating language, and it sinks the field. It's irreversible.

[morpheos137]

If not language what training substrate do you suggest? Also not strong ideas are expressible coherently. You have an ironic pattern in your comments of getting lost in the very language morass you propose to deprecate. If we don't train models on language what do we train them on? I have some ideas of my own but I am interested if you can clearly express yours.

[ACCount37]

One of the main takeaways from The Bitter Lesson was that you should fire your linguists. GPT-2 knows more about human language than any linguist could ever hope to be able to convey.

If you're hitching your wagon to human linguists, you'll always find yourself in a ditch in the end.