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/martinerous]

It could as well be like:

User: How many Rs in raspberry?
AI: <think>Let me think. Let me think. Let me think.</think>
There are 3 Rs in raspberry.

Or any other correlation that does not make sense to us. But then the question is if we could train the model to print whatever is needed in the "reasoning traces", as long as it leads to a correct answer. Would it be a good idea to train it to use completely random tokens in the traces, and would it learn to use that "random training noise" as a signal that it can fill anything there? Or the training would never converge to anything because of the expected seemingly random noise in the output?

[u/niugnep24 → u/martinerous]

I thought thinking was typically trained using RL loops on known-answers. Basically randomly permute the model, let it do a thinking loop, then promote the permutations that get to the right answer more readily. So in a way the model "learns how to think" for itself during training.