Tiny Model, Big Logic: Diversity-Driven Optimization Elicits Large-Model
  Reasoning Ability in VibeThinker-1.5B
Authors: name: Sen Xu; name: Yi Zhou; name: Wei Wang; name: Jixin Min; name: Zhibin Yin; name: Yingwei Dai; name: Shixi Liu; name: Lianyu Pang; name: Yirong Chen; name: Junlin Zhang
Published: 2025-11-09 04:37:36

Abstract: Challenging the prevailing consensus that small models inherently lack robust
reasoning, this report introduces VibeThinker-1.5B, a 1.5B-parameter dense
model developed via our Spectrum-to-Signal Principle (SSP). This challenges the
prevailing approach of scaling model parameters to enhance capabilities, as
seen in models like DeepSeek R1 (671B) and Kimi k2 (>1T). The SSP framework
first employs a Two-Stage Diversity-Exploring Distillation (SFT) to generate a
broad spectrum of solutions, followed by MaxEnt-Guided Policy Optimization (RL)
to amplify the correct signal. With a total training cost of only $7,800,
VibeThinker-1.5B demonstrates superior reasoning capabilities compared to
closed-source models like Magistral Medium and Claude Opus 4, and performs on
par with open-source models like GPT OSS-20B Medium. Remarkably, it surpasses
the 400x larger DeepSeek R1 on three math benchmarks: AIME24 (80.3 vs. 79.8),
AIME25 (74.4 vs. 70.0), and HMMT25 (50.4 vs. 41.7). This is a substantial
improvement over its base model (6.7, 4.3, and 0.6, respectively). On
LiveCodeBench V6, it scores 51.1, outperforming Magistral Medium's 50.3 and its
base model's 0.0. These findings demonstrate that small models can achieve
reasoning capabilities comparable to large models, drastically reducing
training and inference costs and thereby democratizing advanced AI research.