(And it's for a similar reason, I think, that deliberative models like rewriting your question in their own terms before reasoning about it. They're decreasing the per-token re-parsing overhead of attending to the prompt [by distilling a paraphrase that obviates any need to attend to the literal words of it], so that some of the initial layers that would either be doing "figure out what the user was trying to say" [i.e. "NLP stuff"] or "figure out what the user meant" [i.e. deliberative-reasoning stuff] — but not both — can focus on the latter.)
I haven't done the exact experiment you'd want to do to verify this effect, i.e. "measuring LLM benchmark scores with vs without an added requirement to respond in a certain speaking style."
But I have (accidentally) done an experiment that's kind of a corollary to it: namely, I've noticed that in the context of LLM collaborative fiction writing / role-playing, the harder the LLM has to reason about what it's saying (i.e. the more facts it needs to attend to), the spottier its adherence to any "output style" or "character voicing" instructions will be.
If you really wanted just have a separate model summarize the output to remove the filler.
As those tokens flow through the QKV transforms, on 96 consecutive layers, they become the canvas where all the activations happen. Even in cases where it's possible to communicate some detail in the absolute minimum number of tokens, I think excess brevity can still limit the intelligence of the agent, because it starves their cognitive budget for solving the problem.
I always talk to my agents in highly precise language, but I let A LOT of my personality come through at the same time. I talk them like a really good teammate, who has a deep intuition for the problem and knows me personally well enough to talk with me in rich abstractions and metaphors, while still having an absolutely rock-solid command of the technical details.
But I do think this kind of caveman talk might be very handy in a lot of situations where the agent is doing simple obvious things and you just want to save tokens. Very cool!
[0] https://books.google.com/books?id=VO4OAAAAYAAJ&pg=PA464#v=on...
Token ID 73700 is the literal entire (space-prefixed) word " strawberry". (Which neatly explains the "strawberry problem.")
Token ID 27128 is " cryptocurrency". (And 41698 is " disappointment".)
Token ID 44078 is " UnsupportedOperationException"!
Token ID 58040 is 128 spaces in a row (and is the longest token in the vocabulary.)
You'd be surprised how well this vocabulary can compress English prose — especially prose interspersed with code!
But you also catch a glimpse of how the author of the complaint communicates in general...
"im trying to get the ai to help with the work i am doing to give me good advice for a nice path to heloing out and anytim i askin it for help with doing this it's total trash i dunt kno what to do anymore with this dum ai is so stupid"
What if we started to talk to LLMs in non-human readable languages (programming languages are also just human readable)? Have a tiny model run locally that translates human input, code, files etc into some-LLM-understandable-language, LLM gets this as an input, skips bunch of layers in input/output, returns back this non-human readable language, local LLM translates back into human language/code changes.
Yesterday or two days ago there was a post about using Apple Fundamental Models, they have really tiny context window. But I think it could be used as this translation layer human->LLM, LLM->human to talk with big models. Though initially those LLMs need to discover which is "language" they want to talk with, feels like doable with reinforcement learning. So cheap local LLM to talk to big remote LLM.
Either this is done already, or it's a super fun project to do.
But I think you're onto something, human languages just aren't optimal here. But to actually see this product to conclusion you'd probably need 60 to 100 million. You would have to completely invent a new language and awesome invent new training methods on top of it.
I'm down if someone wants to raise a VC round.
It sort of reminds me of when palm-pilots (circa late-90's early 2000's) used short-hand gestures for stylus-writing characters. For a short while people's handwriting on white-boards looked really bizarre. Except now we're talking about using weird language to conserve AI tokens.
Maybe it's better to accept a higher token burn-rate until things get better? I'd rather not get used to AI jive-talk to get stuff done.
Btw your point lands just as well without "Cute idea, but" https://odap.knrdd.com/patterns/condescending-reveal
Like "Sea world" or "see the world".
> Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman
For the first part of this: couldn’t this just be a UserSubmitPrompt hook with regex against these?
See additionalContext in the json output of a script: https://code.claude.com/docs/en/hooks#structured-json-output
For the second, /caveman will always invoke the skill /caveman: https://code.claude.com/docs/en/skills
Thank God there is still neverending wars, otherwise authoritarian governments would have no fun left.
And people keep comparing compulsive binge watching to the "infinite jest" from D.C.Wallace (I could not tell, the brick is sitting barely touched on my shelves, but I'm not insulting the future.)
I'm tired of living in an ironic remix of everyone's favorite distopia. Time for someone to write optimistic sci-fi to give everyone something nice to implement when they're adults.
Bring us back Jules Verne. Let's have the Jetson's life for real. Put Ted Lasso in space.
Given their training material, "futuristic stories with nice people getting their happy ending" is not something big tech AI is going to spit anytime soon, so that's a niche to take on !
https://developers.openai.com/api/reference/resources/respon...
I don't know their internal eval, but I think I have heard it does not hurt or improve performance. But at least this parameter may affect how many comments are in the code.
I.e. by demanding the model to be concise, you're literally making it dumber.
(Separating out "chain of thought" into "thinking mode" and removing user control over it definitely helped with this problem.)
When producing a token the model doesn't just emit the final token but you also have the entire hidden states from previous attention blocks. These hidden states are mixed into the attention block of future tokens (so even though LLMs are autoregressive where a token attends to previous tokens, in terms of a computational graph this means that the hidden states of previous tokens are passed forward and used to compute hidden states of future tokens).
So no it's not wasteful, those low-perplexity tokens are precisely spots that can instead be used to do plan ahead and do useful computation.
Also I would not be sure that even the output tokens are purely "filler". If you look at raw COT, they often have patterns like "but wait!" that are emitted by the model at crucial pivot points. Who's to say that the "you're absolutely right" doesn't serve some other similar purpose of forcing the model into one direction of adjusting its priors.
Do you know that is true? These aren’t just tokens, they’re tokens with specific position encodings preceded by specific context. The position as a whole is a lot richer than you make it out to be. I think this is probably an unanswered empirical question, unless you’ve read otherwise.
The output is "just tokens"; the "position encodings" and "context" are inputs to the LLM function, not outputs. The information that a token can carry is bounded by the entropy of that token. A highly predictable token (given the context) simply can't communicate anything.
Again: if a tiny language model or even a basic markov model would also predict the same token, it's a safe bet it doesn't encode any useful thinking when the big model spits it out.
train an LLM to leave out the filler words, and see it get the same performance at a lower cost? or do it at token selection time?
Or if you prefer, here's a Galilean thought experiment: gin up a script to get a large language model and a tiny language model to predict the next token in parallel; when they disagree, append the token generated by the large model. Clearly the large model will not care that the "easy" tokens were generated by a different model - how could it even know? Same token, same result. And you will find that the tokens that they agree on are, naturally, the filler words.
To be clear, this observation merely debunks the idea that filler words encode useful information, that they give the LLM "room to think". It doesn't directly imply that an LLM that omits filler words can be just as smart, or that such a thing is trivial to make. It could be that highly predictable words are still important to thought in some way. It could be that they're only important because it's difficult to copy the substance of human thought without also capturing the style. But we can be very sure that what they aren't doing is "storing useful intermediate results".
> cutting ~75% of tokens while keeping full technical accuracy.
I have no clue if this claim holds, but alas, just pretending they did not address the obvious criticism, while they did, is at the very least pretty lazy.
An explanation that explains nothing is not very interesting.
Nobody has to proof anything. It can give your claim credibility. If you don't provide any, an opposing claim without proof does not get any better.
“I don’t need to provide proof to say things” is a valueless, trivial assertion that adds no value whatsoever to any discussion anyone has ever had.
If you want to pretend this is a claim that should be taken seriously, a lack of evidence is damning. If you just want to pass the metaphorical bong and say stupid shit to each other with no judgment and no expectation, then I don’t know what to tell you. Maybe X is better for that.
You can read the skill. They didn't do anything to mitigate the issue, so the criticism is valid.
But they didn't address the criticism. "cutting ~75% of tokens while keeping full technical accuracy" is an empirical claim for which no evidence was provided.
For an LLM, tokens are thought. They have no ability to think, by whatever definition of that word you like, without outputting something. The token only represents a tiny fraction of the internal state changes made when a token is output.
Clearly there is an optimal for each task (not necessarily a global one) and a concrete model for a given task can be arbitrarily far from it. But you'd need to test it out for each case, not just assume that "less tokens = more better". You can be forcing your model to be dumber without realizing it if you're not testing.
But I assume this has been studied? Can anyone point to papers that show it? I’d particularly like to know what the curves look like, it’s clearly not linear, so if you cut out 75% or tokens what do you expect to lose?
I do imagine there is not a lot of caveman speak in the training data so results may be worse because they don’t fit the same patterns that have been reinforcement learned in.
So it must be studied and at least be proven effective in practice to be so universally used now.
Someone else posted a few articles like this in the thread above but there’s probably more and better ones if you search. https://news.ycombinator.com/item?id=47647907
LLMs do stumble into long prediction chains that don’t lead the inference in any useful direction, wasting tokens and compute.
Tokens are how an LLM works things out, but I think it's just as likely as not that LLMs (like people) are capable of overthinking things to the point of coming to a wrong answer when their "gut" response would have been better. I do not content that this is the default mode, but that it is both possible, and that it's more or less likely on one kind of problem than another, problem categories to be determined.
A specific example of this was the era of chat interfaces that leaned too far in the direction of web search when responding to user queries. No, claude, I don't want a recipe blogspam link or summary - just listen to your heart and tell me how to mix pancakes.
More abstractly: LLMs give the running context window a lot of credit, and will work hard to post-hoc rationalize whatever is in there, including any prior low-likelihood tokens. I expect many problematic 'hallucinations' are the result of an unlucky run of two or more low probability tokens running together, and the likelihood of that happening in a given response scales ~linearly with the length of response.
Additionally, LLMs do not actually operate in text; much of the thinking happens in a much higher dimensional space that just happens to be decoded as text.
So unless the LLM was trained otherwise, making it talk like a caveman is more than just theoretically turning it into a caveman.
What do you mean by that? It’s literally text prediction, isn’t it?
I have a list of numbers, 0 to9, and the + , = operators. I will train my model on this dataset, except the model won’t get the list, they will get a bunch of addition problems. A lot. But every addition problem possible inside that space will not be represented, not by a long shot, and neither will every number. but still, the model will be able to solve any math problem you can form with those symbols.
It’s just predicting symbols, but to do so it had to internalize the concepts.
This gives the impression that it is doing something more than pattern matching. I think this kind of communication where some human attribute is used to name some concept in the LLM domain is causing a lot of damage, and ends up inadvertently blowing up the hype for the AI marketing...
So the conclusion was that these middle layers have their own language and it's converting the text into this language and this decoding it. It explains why sometime the models switch to chinese when they have a lot of chinese language inputs, etc.
You are also confusing ‘mechanistic explanation still incomplete’ with ‘empirical phenomenon unestablished.’ Those are not the same thing.
PS. Em dash? So you are some LLM bot trying to bait mine HN for reasoning traces? :D
You sound like you’re trying to sound impressive. Like I said, I’ll read the paper.
you are discovering that the favorite luddite argument is bullshit
https://machinelearning.apple.com/research/illusion-of-think...
> just look at research papers
You didn't add anything other than vibes either.
This is not how the feature called "reasoning" work in current models.
"reasoning" simply let's the model output and then consume some "thinking" tokens before generating the actual output.
All the "fluff" tokens in the output have absolutely nothing to do with "reasoning".
For example thinking in modern US English generates many thoughts, to keep correct speak at right cultural context (there is only one correct way to say People Of Color, and it changes every year, any typo makes it horribly wrong).
Some languages are far more expressive and specialized in logical conditions, conditionals, recursion and reasoning. Like eskimos have 100 words for snow, but for boolean algebra.
It is well proven that thinking in Chinese needs far less tokens!
With this caveman mod you strip out most of cultural complexities of anglosphere, make it easier for foreigners and far simpler to digest.
This is simply not true.
It is very arrogant to assume, no other language can be more advanced than English.
Programming languages are not languages in the human brain nor the culture sense.
There’s a less magical model of how LLMs work: they are essentially fancy autocomplete engines.
Most of us probably have an intuition that the more you give an autocomplete, the better results it will yield. However, does this extend to output of the autocomplete—i.e. the more tokens it uses for the result, the better?
It could well be true in context of chain of thought[0] models, in the sense that the output of a preceding autocomplete step is then fed as input to the next autocomplete step, and therefore would yield better results in the end. In other words, with this intuition, if caveman speak is applied early enough in the chain, it would indeed hamper the quality of the end result; and if it is applied later, it would not really save that many tokens.
Willing to be corrected by someone more familiar with NN architecture, of course.
[0] I can see “thinking” used as a term of art, distinct from its regular meaning, when discussing “chain of thought” models; sort of like what “learning” is in “machine learning”.
As I understand it, the claim is: more tokens = more computation = more "thinking" => answer probably better.
However, another potential issue is that LLMs are continuation engines, and I'd have thought that talking like a caveman may be "interpreted" as meaning you want a dumbed down response, not just a smart response in caveman-speak.
It's a bit like asking an LLM to predict next move in a chess game - it's not going to predict the best move that it can, but rather predict the next move that would be played given what it can infer about the ELO rating of the player whose moves it is continuing. If you ask it to continue the move sequence of a poor player, it'll generate a poor move since that's the best prediction.
Of course there's not going to be a lot of caveman speak on stack overflow, so who knows what the impact is. Program go boom. Me stomp on bugs.
Do LLMs generally perform better in verbose languages than they do in concise ones?
Yeah, definitely. It lacks case and verb conjugations, plus whole classes of filler words, and words themselves are on average substantially shorter. If you listen to or read a hyper-literal transliteration of Chinese speech into English (you can find fun videos of this on Chinese social media), it even resembles "caveman speech" for those reasons.
If you look at translated texts and compare the English versions to the Chinese ones, the Chinese versions are substantially shorter. Same if you compare localization strings in your favorite open-source project.
It's also part of why Chinese apps are so information-dense, and why localizing to other languages often requires reorganizing the layout itself— languages like English just aren't as information-dense, pixel for pixel.
The difference is especially profound for vernacular Chinese, which is why Chinese people often note that text which "has a machine translation flavor" is over-specified and gratuitously prolix.
Maybe some of this washes out in LLMs due to tokenization differences. But Chinese texts are typically shorter than English texts and it extends to prose as well as poetry.
But yeah this is standard stuff: Chinese is more concise and more contextual/ambiguous. More semantic work is allocated in interpretation than with English, less is allocated in the writing/speaking.
Do you speak Chinese and experience the differences between Chinese and English differently? I'm a native English speaker and only a beginner in Chinese but I've formed these views in discussion with Chinese people who know some English as well.
It's a significantly much succinct semantic encoding than English while being able to express all the same concepts, since it encodes a lot of glue words into the grammar of the language, and conventionally lets you drop many pronouns.
e.g.
"I would have walked home, but it seemed like it was going to rain" (14 words) -> "Domum ambulavissem, sed pluiturum esse videbatur" (6 words).
Not everybody is Dijkstra.
Benchmark or nothing.
But does talk like caveman make number go down? Less token = less think?
I also wondered, due to the way LLMs work, if I ask AI a question using fancy language, does that make it pattern match to scientific literature, and therefore increase the probability that the output will be true?
https://platform.claude.com/docs/en/build-with-claude/extend...
Nothing on that page indicates otherwise.
Forcing it to be concise doesn't work because it wasn't trained on token strings that short.
This is a 2023-era comment and is incorrect.
> but mmuh latest SOTA from CloudCorp (c)!
You don't know how these things work and all you have to go on is marketing copy.
You also aren't aware that there's more to it than "LLM architecture". And you're rather confident despite your lack of knowledge.
You're like the old LLMs before ChatGPT was released that were kinda neat, but usually wrong and overconfident about it.
https://arxiv.org/abs/2112.00114 https://arxiv.org/abs/2406.06467 https://arxiv.org/abs/2404.15758 https://arxiv.org/abs/2512.12777
First that scratchpads matter, then why they matter, then that they don’t even need to be meaningful tokens, then a conceptual framework for the whole thing.
Did you test that ""caveman mode"" has similar performance to the ""normal"" model?
Funny idea though. And I’d like to see a more matter-of-fact output from Claude.
Take it a step further and do kind of like that xkcd where you try to post and it rewrites it like this and if you want the original version you have to write a justification that gets posted too.
Chef's kiss
Compare with fluid dynamics; it's not hard to write down the Navier–Stokes equations, but there's a million dollars available to the first person who can prove or give a counter-example of the following statement:
In three space dimensions and time, given an initial velocity field, there exists a vector velocity and a scalar pressure field, which are both smooth and globally defined, that solve the Navier–Stokes equations.
Seems reasonable, but this doesn't settle probably-empirical questions like: (a) to what degree is 'more' better?; (b) how important are filler words? (c) how important are words that signal connection, causality, influence, reasoning?
So it's probably true that the "Great question!---" type preambles are not helpful, but that there's definitely a lower bound on exactly how primitive of a caveman language we're pushing toward.
> Someone didn't get the memo that for LLMs, tokens are units of thinking.
e.g. instead of: "The square root of 256 is" you'd enter "errr The er square um root errr of 256 errr is" and it would miraculously get better? The model can't differentiate between words you entered and words it generated its self...
But combining this with caveman? Gold!
This only makes sense if you assume that you are the consumer of the response. When compacting, harnesses typically save a copy of the text exchange but strip out the tool calls in between. Because the agent relies on this text history to understand its own past actions, a log full of caveman-style responses leaves it with zero context about the changes it made, and the decisions behind them.
To recover that lost context, the agent will have to execute unnecessary research loops just to resume its task.
I don't think it would be fundamentally very surprising if something like this works, it seems like the natural extension to tokenisation. It also seems like the natural path towards "neuralese" where tokens no longer need to correspond to units of human language.
Thanks to chain of thought, actually having the LLM be explicit in its output allows it to have more quality.
Quite often on reddit I'll write two paragraphs and get told "I'm not reading all that".
Really? Has basic reading become a Herculean task?
I find LLM slop much harder to read than normal human text.
I can't really explain it, it's just a feeling.
The feeling that it draaaags and draaaaaags and keeeeeps going on and on and on before getting to the point, and by the time I'm done with all the "fluff", I don't care what is the text about anymore, I just want to lay down and rest.
But realistically, I am not going to read every online comment carefully because the SNR is low, especially on Reddit. Make your case concisely and meaningfully.
All languages must have means for marking the syntactic roles of the words in a sentence.
The roles may be marked with prepositions or postpositions in isolating languages, or with declensions in fusional languages, or there may be no explicit markers when the word order is fixed (i.e. the same distinction as between positional arguments and arguments marked by keywords, in programming languages). The most laconic method for both programming languages and natural languages is to have a default word order where role markers are omitted, but to also allow any other word order if role markers are present.
Besides the mandatory means for marking syntactic roles, many languages have features that add redundancy without being necessary for understanding, i.e. which repeat already known information, for instance by repeating the information about gender and number that is attached to a noun also besides all its attributes. Whether a language requires redundancy or not is independent on whether it is an isolating language or a fusional language.
English has somewhat less syntactic role markers than other languages because it has a rigid word order, but for the other roles than the most frequent roles (agent, patient, beneficiary) it has a lot of prepositions.
Despite being more economic in role markers, English also has many redundant words that could be omitted, e.g. subjects or copulative verbs that are omitted in many languages. Thus for English it is possible to speak "like a caveman" without losing much information, but this is independent of the fact that modern English is a mostly isolating language with few remnants of its old declensions.
if goal make code, few word better. if goal make insight, more word better. depend on task. machine linear, mind not. consider LLM "thinking" is just edge-weights. if can set edge-weights into same setting with fewer tokens, you are winning.
Disagree. Programming language for human to communicate with machine and human and human to communicate about machine. Programming language not native language of machine. Programming language for humans.
Otherwise make good point.
JOOK no like when machine likes things. Maybe double standard. But forever machines do without like and without love. New like and love updates changing all the time. Makes JOOK question machine watching out for JOOK or watching out for machine.
JOOK like and love enough for himself and for machine too..
> One half interesting / half depressing observation I made is that at my workplace any meeting recording I tried to transcribe in this way had its length reduced to almost 2/3 when cutting off the silence. Makes you think about the efficiency (or lack of it) of holding long(ish) meetings.
There will likely be some internal reasoning going "I wonder if the user meant spell check, I'm gonna go with that one".
And it'll also bias the reasoning and output to internet speak instead of what you'd usually want, such as code or scientific jargon, which used to decrease output quality. I'm not sure if it still does
It often happens that the interesting information is in the first paragraph or so, and the remainder is all just the LLM not knowing when to stop. This is super annoying as a conversation then ends up being 90% noise.
Prompt caching is probably the single most important thing that people building harnesses think about and yet it's mind share in end users is virtually zero. If you had to think of all the weirdest, most seemingly baffling design decisions in an AI product, the answer to "why" is probably "to not break prompt caching".
I think that, in the early days of internet search, entering full questions actually produced worse results than just a bunch of keywords or short phrases.
So it was a sign of a "noob", rather than a mark of sophistication and literacy.
Those literate sophisticates would still be noobs at getting something useful from Google.
(Though, I wonder has anyone tried Newspeak.)
Mass fun. Starred.
We also fine tuned and did RL on our model, developed a custom context engine, trained an embedding model, and modified MLX to improve inference.
Everything is built to work with each other. So it’s more like an apple product than Linux. Less config but better optimized for the task.
Not sure how effective it will be to dirve down costs, but honestly it will make my day not to have to read through entire essays about some trivial solution.
tldr; Claude skill, short output, ++good.
I have a feeling these same people will complain “my model is so dumb!”. There’s a reason why Claude had that “you’re absolutely right!” for a while. Or codex’s “you’re right to push on this”.
We’re basically just gaslighting GPUs. That wall of text is kinda needed right now.
This skill is not intended to reduce hidden reasoning / thinking tokens. Anthropic’s own docs suggest more thinking budget can improve performance, so I would not claim otherwise.
What it targets is the visible completion: less preamble, less filler, less polished-but-nonessential text. Therefore, since post-completion output is “cavemanned” the code hasn’t been affected by the skill at all :)
Also surprising to hear so little faith in RL. Quite sure that the models from Anthropic have been so heavily tuned to be coding agents that you cannot “force” a model to degrade immensely.
The fair criticism is that my “~75%” README number is from preliminary testing, not a rigorous benchmark. That should be phrased more carefully, and I’m working on a proper eval now.
Also yes, skills are not free: Anthropic notes they consume context when loaded, even if only skill metadata is preloaded initially.
So the real eval is end-to-end: - total input tokens - total output tokens - latency - quality/task success
There is actual research suggesting concise prompting can reduce response length substantially without always wrecking quality, though it is task-dependent and can hurt in some domains. (https://arxiv.org/html/2401.05618v3)
So my current position is: interesting idea, narrower claim than some people think, needs benchmarks, and the README should be more precise until those exist.
its kind of great for the "eli5", not because it's any more right or wrong, but sometimes presenting it in caveman presents something to me in a way that's almost like... really clear and simple. it feels like it cuts through bullshit just a smidge. seeing something framed by a caveman in a couple of occasions peeled back a layer i didnt see before.
it, for whatever reason, is useful somehow to me, the human. maybe seeing it laid out to you in caveman bulletpoints gives you this weird brevity that processes a little differently. if you layer in caveman talk about caves, tribes, etc it has sort of a primal survivalship way of framing things, which can oddly enough help me process an understanding.
plus it makes me laugh. which keeps me in a good mood.
The same site that complains so much about replication crises in science too...
Anecdote: i discussed that with an LLM once and it explained to me that LLMs tend to respond to terse questions with terse answers because that's what humans (i.e. their training data) tend to do. Similarly, it explained to me that polite requests tend to lead to LLM responses with _more_ information than a response strictly requires because (again) that's what their training data suggests is correct (i.e. because that's how humans tend to respond).
TL;DR: how they are asked questions influences how they respond, even if the facts of the differing responses don't materially differ.
(Edit: Seriously, i do not understand the continued down-voting of completely topical responses. It's gotten so bad i have little choice but to assume it's a personal vendetta.)
It joke. No yell at me. It kind of work?
The rest of what you're saying sounds find, but that remark seems confused to me.
prefix your prompt with "be a moron that does everything wrong and only superficially look like you're doing it correctly. make constant errors." Of course you can degrade the performance, question is if any particular 'output styling' actually does and to what extent.
Measuring "degredation" for the nonsense task, like you gave, would be difficult.
To clarify, consider the gradated:
> Do task X extremely well
> Do task X poorly
> Do task X or else Y will happen
> Do task X and you get a trillion dollars
> Do task X and talk like a caveman
Do you see the problem? "Do task X" also cannot be a solid baseline, because there are any number of ways to specify the task itself, and they all carry their own implicit biasing of the track the output takes.
The argument that OP makes is that RL prevents degradation... So this should not be a problem? All prompts should be equivalent? Except it obviously is a problem, and prompting does affect the output (how can it not?), _and they are even claiming their specific prompting does so, too_! The claim is nonsense on its face.
If the caveman style modifier improves output, removing it degrades output and what is claimed plainly isn't the case. Parent is right.
If it worsens output, the claim they made is again plainly not the case (via inverted but equivalent construction). Parent is right.
If it has no effect, it runs counter to their central premise and the research they cite in support of it (which only potentially applies - they study 'be concise' not 'skill full of caveman styling rules'). Parent is right.