3B的小模型在数学和代码推理上快追上大模型了，适合部署在低算力场景，值得关注。
VibeThinker-3B是仅3B参数的小模型，在AIME26上取得94.3分，在LiveCodeBench v6上Pass@1达80.2，在未见过的LeetCode比赛中正确率96.1%。其训练基于Qwen2.5-Coder，结合课程SFT、多领域RL、离线自蒸馏和最终RL指导阶段。结果表明，部分可验证推理能力可被高效压缩到小密集模型中。
Crazy: A 3B model is now reaching highly competiti…
Crazy: A 3B model is now reaching highly competitive results on verifiable reasoning tasks.
VibeThinker-3B scores 94.3 on AIME26, 80.2 Pass@1 on LiveCodeBench v6, and 96.1% on unseen LeetCode contests.
The gains appear to come primarily from post-training on top of Qwen2.5-Coder: curriculum SFT, multi-domain RL, offline self-distillation, and a final RL-based instruct stage.
The core implication: certain forms of verifiable reasoning may be highly compressible into small dense models.
Frontier-scale models still matter for broad knowledge and general-purpose capability, but compact reasoning models are becoming a serious complementary path.
Love to see it!