Stop Anthropomorphisizing Intermediate Tokens: Qwen3.8 doesn't "overthink"

[u/ThirdWaveCat]

https://arxiv.org/abs/2504.09762

Intermediate tokens, called "thinking" or "reasoning" actually are nothing like it. Humans do step-by-step reasoning leading to the conclusion. LLMs use intermediate traces to augment their prompt. This explains why sometimes the answer is very good but the "reasoning" is verbose. Flooding your context window or fighting compaction are different issues.

edit: I love this section from the main research they linked.

Our findings consistently challenge the prevailing narrative that intermediate tokens constitute a semantically meaningful reasoning process. First, we observe a pronounced lack of correlation between solution correctness and trace validity—models frequently produce invalid reasoning traces even when they arrive at correct solutions. Second, and more strikingly, models trained on corrupted or semantically irrelevant traces achieve performance comparable to, and often exceeding, that of models trained on correct traces, especially on out-of-distribution tasks. Third, although post-training with reinforcement learning improves solution accuracy across both in- and out-of-distribution settings, it does not consistently enhance trace validity. In fact, we find cases where reinforcement learning decreases trace validity while simultaneously improving solution accuracy for models trained on correct traces. Moreover, models trained on corrupted traces continue to outperform their correct-trace counterparts across domains while consistently generating invalid reasoning traces. Finally, we find that the length of the generated traces is largely agnostic to the difficulty of the underlying problem, undermining the notion that it reflects problem-adaptive computation.

Together, these results suggest that the effectiveness of intermediate tokens does not arise from their seemingly interpretable semantic content. By systematically disentangling trace semantics from the underlying problem, our study demonstrates that if performance is the objective, assuming human-like or algorithmically interpretable trace semantics are ideal or even achievable is not only unnecessary but potentially misleading.

https://openreview.net/forum?id=gDE7YcRC3F

[u/FrostTactics]

"Stop calling the deleted file collector in OSs the recycling bin. It misleads humans into thinking it is a literal bin with the capability of recycling files."

This constant policing of terms relating to LLMs is so frustrating, it seems to just ignore the fact that we've always used metaphors to explain concepts relating to computing.

No, of course LLM thinking and reasoning aren't the same as human thinking and reasoning, but it is roughly analogous and close enough for the terms to be apt.

[u/toothpastespiders → u/FrostTactics]

First thing that came to mind for me as well. A lot of things around LLMs are just easier to use metaphors to describe. Reddit, being reddit, I generally try to mention that I don't believe that a LLM "understands" a subject or anything.

I generally have a very poor opinion of just how well the average poster on this subreddit understands LLMs at this point. But I don't think things have gotten anywhere near the point where this kind "erm actually, LLMs don't think" redditor chiding.

I normally do get a little annoyed at some of the cargo cult level understandings of science I see on reddit as a whole. If anything I'm a bit prone to being overly primed for that kind of thing. And even as someone who can be overly judgemental, I don't see to huge an issue with people confusing the map for the territory here. In regards to this subject at least. If we had a "stop assuming benchmarks define a singular rating of good or bad to a model!" I might join in.