Counteracting Matthew Effect in Self-Improvement of LVLMs through
  Head-Tail Re-balancing
Authors: name: Xin Guo, name: Zhiheng Xi, name: Yiwen Ding, name: Yitao Zhai, name: Xiaowei Shi, name: Xunliang Cai, name: Tao Gui, name: Qi Zhang, name: Xuanjing Huang

Self-improvement has emerged as a mainstream paradigm for advancing the
reasoning capabilities of large vision-language models (LVLMs), where models
explore and learn from successful trajectories iteratively. However, we
identify a critical issue during this process: the model excels at generating
high-quality trajectories for simple queries (i.e., head data) but struggles
with more complex ones (i.e., tail data). This leads to an imbalanced
optimization that drives the model to prioritize simple reasoning skills, while
hindering its ability to tackle more complex reasoning tasks. Over iterations,
this imbalance becomes increasingly pronounced--a dynamic we term the "Matthew
effect"--which ultimately hinders further model improvement and leads to
performance bottlenecks. To counteract this challenge, we introduce four
efficient strategies from two perspectives: distribution-reshaping and
trajectory-resampling, to achieve head-tail re-balancing during the
exploration-and-learning self-improvement process. Extensive experiments on
Qwen2-VL-7B-Instruct and InternVL2.5-4B models across visual reasoning tasks
demonstrate that our methods consistently improve visual reasoning
capabilities, outperforming vanilla self-improvement by 3.86 points on average.