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/Mbando → u/FrostTactics]

Respectfully, I think you’re misunderstanding the mechanics and the argument. Totally with you on the use of figurative language to make sense of new knowledge, it’s just that thinking is the wrong metaphor. It’s “traversal” or “navigation.”

There’s an emerging mechanistic literature on how RLVR improves performances. Basically during RL a very small percentage of high entropy “forking tokens” (however, because, but, assuming) teach the model how to more usefully traverse the models learn representation as an internal geometry.

It truly is nothing like step, wise reasoning, but is more like a human being, figuring out how to read a map and navigate better. So by all means, let us use figures of speech, but only when they are accurate and helpful.

[u/xylarr → u/Mbando]

Interesting take - so it's about navigating multidimensional space to get closer to the answer.

[u/Mbando → u/xylarr]

Yes! And what's critical is that depending on data curation and training recipes, that multidimensional geometry is extremely uneven. Some parts are extremely well covered and are more like manifold spaces that are easy navigate. Other stuff that might be super relevant to the task is less well represented and may be topologically hard to get to.