Diving into Self-Evolving Training for Multimodal Reasoning
Authors: name: Wei Liu, name: Junlong Li, name: Xiwen Zhang, name: Fan Zhou, name: Yu Cheng, name: Junxian He

Reasoning ability is essential for Large Multimodal Models (LMMs). In the
absence of multimodal chain-of-thought annotated data, self-evolving training,
where the model learns from its own outputs, has emerged as an effective and
scalable approach for enhancing reasoning abilities. Despite its growing usage,
a comprehensive understanding of self-evolving training, particularly in the
context of multimodal reasoning, remains limited. In this paper, we delve into
the intricacies of self-evolving training for multimodal reasoning, pinpointing
three key factors: Training Method, Reward Model, and Prompt Variation. We
systematically examine each factor and explore how various configurations
affect the training's effectiveness. Our analysis leads to a set of best
practices for each factor, aimed at optimizing multimodal reasoning.
Furthermore, we explore the Self-Evolution Dynamics during training and the
impact of automatic balancing mechanisms in boosting performance. After all the
investigations, we present a final recipe for self-evolving training in
multimodal reasoning, encapsulating these design choices into a framework we
call MSTaR (Multimodal Self-evolving Training for Reasoning), which is
universally effective for models with different sizes on various benchmarks,
e.g., surpassing the pre-evolved model significantly on 5 multimodal reasoning
benchmarks without using additional human annotations, as demonstrated on
MiniCPM-V-2.5 (8B), Phi-3.5-Vision (4B) and InternVL2 (2B). We believe this
study fills a significant gap in the understanding of self-evolving training
for multimodal reasoning and offers a robust framework for future research. Our
policy and reward models, as well as the collected data, is released to
facilitate further investigation in multimodal reasoning.