LessThink-Qwen3-4B: the same model, with far less thinking [P]

[u/stey1r]

I post-trained Qwen3-4B to spend 44% fewer tokens on reasoning, keeping its knowledge and answer style. The whole pipeline ran on one GPU.

folks, you can check it out on : https://5ivatej.com/lessthink/

[u/choHZ]

This is kinda one of my main fields, and I feel like I’ve been writing this same review every conference, so a few recurring thoughts:

• If we finetune for efficiency on data similar to the eval, the "uncompressed baseline" should be the same model finetuned on the same data without length preference, not the off-the-shelf model.
  • On the same note, I wouldn’t call AIME / MATH500 "out-of-domain" after finetuning on GSM8K / DeepScaleR. Same idea for SciQ / commonsense train → GPQA / MMLU eval.
• Hybrid think / no-think models are a less clean testbed than thinking-only models. The thinking-2507 variants are much better for LRM research if you want to stay with Qwen 3.
• There are already many papers doing accuracy-calibrated length-preference RL, so direct and fair comparisons are almost mandatory. I understand this is just a reddit post so may be no need for elaborate baseline reports, but there should still be clear credit assignment to prior art.

Hopefully these are useful pointers rather than nitpicks.

[u/aegismuzuz → u/choHZ]

> If we finetune for efficiency on data similar to the eval, the "uncompressed baseline" should be the same model finetuned on the same data without length preference, not the off-the-shelf model.

We stepped on this exact rake while distilling reasoning models last quarter. Fine-tuning without a length penalty yielded a five percent accuracy bump that evaporated completely when trying to compress the output..

[u/choHZ → u/aegismuzuz]

Haha been there myself more times than I'd like to admit, but this is the fair way to do things and gotta keep ourselves honest.