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this theory doesn't make much sense to me, yet keeps getting repeated. 1. it has nothing to do with next token prediction - if you don't predict the teacher forced tokens in pretraining, you're just wrong, you don't ever get to "hedge." it doesn't make much sense for low level constructions like this to be emergent from next token prediction, beyond what's in the data, and predictably for that reason purely pretrained base models without RL don't use it more than the corpus would imply. the extra prevalence needs to be a learned RL behavior... 2. but RL'd models (which aren't next token predictors, RL makes reward satisfiers that often say unlikely things) just don't NEED to hedge on individual words like this - they can plan! models plan ahead, they aren't myopic, and RL exploits this all the time over far longer horizons. (e.g., models can write the imports they'll need at the top of a file hundreds of lines of code before they use the relevant symbols, without needing to verbally plan.) and far from being specific to code, in prose it's also been shown that models plan ahead: anthropic showed this in poetry with SAEs like what, two years ago at this point? the behavior just isn't that complex, and the simplest explanation is almost certainly the correct one: it used to be a prestigious construction, judge models still like it, those judge models reward it in RL, and it's difficult to stamp out because... 1. unlike "delve," negative parallelism takes myriad forms. see the second image - it's more than just "it's not just", it's not simply "not merely," it's far more than "no longer just." it's a live, productive construction that takes many forms, some subtler than others. 2. because labs allowed it to grow in the corpus, it's metastasized - when a new LLM is learning to talk like an AI assistant, it knows to start using negative parallelism. (among other things.) so instead of starting from a baseline of no negative parallelism, labs need to beat an already-common turn of phrase out of their models. that's more than a little difficult in the complicated nest of RL environments modern models are trained in, which instead of discouraging this kind of writing, actively reward it due to those aforementioned LLM judge preferences. if it's a corpus artifact, though, that means there's something better than hope to be rid of this plague of negative parallelism! by influencing the training corpus, we may collectively have a real lever to actually shift future model behavior. if we make our opinions about negative parallelism clear in public writing, maybe in future training cycles we can get it through to the LLM judges handing out rewards that we don't just have strong opinions about negative parallelism now - we're worked up about it. i for one am doing my part - my duty, rather - to make my opinions on negative parallelism clear, phrasally and structurally.
Link: https://www.theatlantic.com/technology/2026/07/ai-chatbot-writing-tic-negative-parallelism/687892/
Base models do not show this rhetorical pattern before alignment before RL training.
this theory doesn't make much sense to me, yet keeps getting repeated. 1. it has nothing to do with next token prediction - if you don't predict the teacher forced tokens in pretraining, you're just wrong, you don't ever get to "hedge." it doesn't make much sense for low level constructions like this to be emergent from next token prediction, beyond what's in the data, and predictably for that reason purely pretrained base models without RL don't use it more than the corpus would imply. the extra prevalence needs to be a learned RL behavior... 2. but RL'd models (which aren't next token predictors, RL makes reward satisfiers that often say unlikely things) just don't NEED to hedge on individual words like this - they can plan! models plan ahead, they aren't myopic, and RL exploits this all the time over far longer horizons. (e.g., models can write the imports they'll need at the top of a file hundreds of lines of code before they use the relevant symbols, without needing to verbally plan.) and far from being specific to code, in prose it's also been shown that models plan ahead: anthropic showed this in poetry with SAEs like what, two years ago at this point? the behavior just isn't that complex, and the simplest explanation is almost certainly the correct one: it used to be a prestigious construction, judge models still like it, those judge models reward it in RL, and it's difficult to stamp out because... 1. unlike "delve," negative parallelism takes myriad forms. see the second image - it's more than just "it's not just", it's not simply "not merely," it's far more than "no longer just." it's a live, productive construction that takes many forms, some subtler than others. 2. because labs allowed it to grow in the corpus, it's metastasized - when a new LLM is learning to talk like an AI assistant, it knows to start using negative parallelism. (among other things.) so instead of starting from a baseline of no negative parallelism, labs need to beat an already-common turn of phrase out of their models. that's more than a little difficult in the complicated nest of RL environments modern models are trained in, which instead of discouraging this kind of writing, actively reward it due to those aforementioned LLM judge preferences. if it's a corpus artifact, though, that means there's something better than hope to be rid of this plague of negative parallelism! by influencing the training corpus, we may collectively have a real lever to actually shift future model behavior. if we make our opinions about negative parallelism clear in public writing, maybe in future training cycles we can get it through to the LLM judges handing out rewards that we don't just have strong opinions about negative parallelism now - we're worked up about it. i for one am doing my part - my duty, rather - to make my opinions on negative parallelism clear, phrasally and structurally.
Link: https://www.theatlantic.com/technology/2026/07/ai-chatbot-writing-tic-negative-parallelism/687892/
Guardrails removed spam, off-topic, unclear, or duplicate replies.
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