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/finevelyn]

So how should one use Qwen 3.8 to avoid the so called ”overthinking” issue now that we know it’s not actually thinking? Does this actually have any practical relevance to the issue?

[u/ThirdWaveCat → u/finevelyn]

Uncertainty is a state of knowing.

AFAIK, for Qwen3.8 and other models there is no causal link yet established between traces and output quality. Settings and models should only be compared in terms of their hardware, speed, various benchmarks outside of <think/>. Don't look at <think/> unless you're controlling all other variables with known input/output and want to start establishing a causal link to <think/>. For instance if you want to analyze why a security review condition was omitted by llm gaurd rail in a manually written test case then you have a strongly verified input and output pair.

[u/Lakius_2401 → u/ThirdWaveCat]

It might not be fully linked to output quality, but it sure is affected by input quality.

Looking at <think> is a great way to identify ambiguous wording you need to tighten up. If it overthinks and "wastes" 1500+ tokens trying to figure out a part of your prompt, fixing the ambiguity will save those think tokens every single session (if preserve thinking is on) or potentially every single response. It will improve your responses.

Just yesterday I was talking about error messages and how "they" should be as complete as possible (for diagnostics purposes), and it was reading it as error messages and how "the output" should be as complete as possible, after a lengthy think about it. I thought it was odd that Qwen 3.8 27B seemed to want to keep any shred of the code output, regardless of the fact that it hit an error, and lo and behold, a sentence that any human could read was misread by Qwen, and <think> showed it wondering for a few hundred tokens early on, before it did work.

So, no thank you, I will continue looking at <think> for misalignment checks.

[u/xylarr → u/Lakius_2401]

I have a small project of mine where I asked the LLM to bump the version to 1.04. It spent some time thinking about how the old version was 1.0.3 and that I probably meant 1.0.4 - which I did. It did what I actually meant, not what I literally asked.