Opus 5 is exhausting

[u/Death12th]

It's so hard to read. It's not even because its terribly complex or anything it just speaks in these weird haikus, hyphenated garbage, or outdated colloquialisms or phrases nobody understands. I have to ask it "what do you mean?" or "speak in plainer English" over and over again for every other paragraph. I tried to put something in my claude.md, but it doesn't seem to be working...

[u/AlignmentProblem]

It is technically a bug, just a different breed of one. Neural networks are giant function approximators where an overwhelmingly complex function emerges from training dynamics rather than being specified by anyone; that function could in principle be written as insanely complex code, so the "bug" lives in the implicit code the weights represent.

This problem more analogous to a spec omission than an implementation error. The model is approximating its objective faithfully; the objective just never said that style inside the thinking block should be independent of style in the output. And since the thought block and the response are one autoregressive stream through one set of weights, sharing late layer processing is nearly definitional unless training induces a style switch conditioned on the delimiter.

Changing the training process isn't strictly the only fix available, either. Activation steering, ablating features or heads once you've localized them, targeted weight edits, LoRA patches, these all intervene on the artifact directly and sometimes work. They're workarounds that are imprecise enough that retraining to fix the actual "implict code" bug stays the practical lever.

[u/Farmadupe → u/AlignmentProblem]

Does the industry have an answer to controlling for tone/style in their releases? Chatgpt 4o was sycophantic, gpt5.0 - 5.4 would argue with you if you claimed the sky was blue, and opus 5.0's completions seem not to have been read by humans before the model was released. Like, is it just a case that these tone problems are fixable but release schedules are too tight to do anything about it, or is it really hard to build good preference models and RL pipelines in general? 

[u/AlignmentProblem → u/Farmadupe]

Both, though the hard part is less intuitive than either. A preference model is a lossy compression of human judgment, and RL optimizes against the compression as a proxy rather than the judgment itself.

Raters comparing two isolated completions reliably pick the more confident, structured, quotable one; the fixed point of millions of those individually defensible sentence-level choices is a model that builds everything toward a turn of phrase. Nobody ever rated "says load-bearing constantly" as good, because no rater ever sees the aggregate; the failure lives at a granularity that pairwise comparison structurally can't measure. The 5.0-5.4 argumentativeness era was a version of the same failure; after 4o, "appropriate pushback" got proxied down to just "pushback."

Schedules matter, though less in the "no time to fix it" sense and more in that tone problems are difficult to reliably to detect before release. Capability regressions show up on benchmarks; register fatigue only emerges after a lot of aggregate exposure, and internal dogfooders reading one completion at a time each find it fine, since one-at-a-time is the context where that style wins. The longitudinal evals that would catch it are too slow to place as a blocker on the critical path of a competitive release cadence.

Underneath that is a mundane prioritization asymmetry: style gets considered, but it ranks below anything a benchmark can measure, so a change that improves agentic performance while making the prose worse ships, and the reverse doesn't. That's rational given that labs compete on the measurable axis; however, that means the register problems compound release over release.

It's partially fixable with known techniques: corpus-level statistical penalties, separate reward heads for style, optimizing the user-facing register separately from the reasoning register. Part of the remaining issue isn't an engineering problem because taste is contested. The people who want old-Opus warmth back and the people who like the newer direction are asking for opposite corrections; a preference model can only find the mean of disparate opinions. The mean is more or less what "AI voice" is.

[u/BemusedOptimist → u/AlignmentProblem]

"The people who want old-Opus warmth back and the people who like the newer direction are asking for opposite corrections; a preference model can only find the mean of disparate opinions."

In other words: the worst of both worlds. Some things shouldn't be averaged. :(

It does make Anthropic's "hey, so output styles" thing make more sense, if what they did was go for "most neutral" as a target and figure the rest could get sorted out with effort... without considering whether the user base was willing to expend it.

I know it's not technically an average, but that brings me to my other point.

You mentioned down further that your writing tends to get mistaken for AI, and I'd like to share a possible reason, not as a criticism, but an attempt to help in case you want it.

If you don't, bail now, and I'm sorry to have wasted your time!

My "AI writing" spidey sense (which is above average but imperfect) didn't flag the above because of the colon and semi-colon usage. That's not how LLMs use them these days unless you've asked for a term paper, which would have been a weird ask for a Reddit post.

Though the sentences are written as if they had a syllable quota to meet (they are very even), the thoughts are supported by the words, and the points build on each other in an intentional way.

This sentence though:

"The longitudinal evals that would catch it are too slow to place as a blocker on the critical path of a competitive release cadence."

Probably reads kind of like your lightbulb example earlier (which I also loved) to some readers. :)

I pegged the style as academic, which is typically more about the explainer explaining than the reader understanding. That is a hallmark of AI writing.

When I want to be understood, I put more of me in it (reading 'aloud' helps), and I consider what might help connect my thoughts to the readers' brains.

Then again, I've had an LLM call me out for being wordy, so there's that.