SynthRL: Scaling Visual Reasoning with Verifiable Data Synthesis
Authors: name: Zijian Wu, name: Jinjie Ni, name: Xiangyan Liu, name: Zichen Liu, name: Hang Yan, name: Michael Qizhe Shieh

Vision-language models (VLMs) trained via reinforcement learning with
verifiable reward (RLVR) have shown notable progress in scaling test-time
compute effectively. In this work, we investigate how synthesized RL data can
further improve RLVR. To this end, we propose SynthRL-a scalable and
guaranteed pipeline for automatic data scaling in reasoning-oriented RL
training. SynthRL comprises three key stages: (1) selecting seed questions with
appropriate distribution, (2) augmenting them into more challenging variants
while preserving the original answers, and (3) a guaranteed verification stage
that ensures near-perfect correctness and difficulty enhancement. Our empirical
experiments demonstrate SynthRL's scalability and effectiveness. When applied
to the MMK12 dataset, SynthRL synthesizes over 3.3K additional verifiable,
challenging questions from approximately 8K seed samples. Models trained with
our synthesized data achieve consistent gains across five out-of-domain visual
math reasoning benchmarks, with a significant improvement over baseline models
trained on seed data alone. Notably, detailed analysis reveals that the gains
are more pronounced on the most challenging evaluation samples, highlighting
SynthRL's effectiveness in eliciting deeper and more complex reasoning
patterns.