Summary of Diving Into Self-evolving Training For Multimodal Reasoning, by Wei Liu et al.
Diving into Self-Evolving Training for Multimodal Reasoning
by Wei Liu, Junlong Li, Xiwen Zhang, Fan Zhou, Yu Cheng, Junxian He
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the intricacies of self-evolving training for multimodal reasoning in Large Multimodal Models (LMMs). Self-evolving training involves a model learning from its own outputs, which has been shown to enhance reasoning abilities. The authors identify three key factors that affect the effectiveness of self-evolving training: Training Method, Reward Model, and Prompt Variation. They systematically examine each factor and provide best practices for optimizing multimodal reasoning. Additionally, they explore Self-Evolution Dynamics during training and the impact of automatic balancing mechanisms on performance. The study culminates in a framework called MSTaR (Multimodal Self-evolving Training for Reasoning), which is universally effective for models with different sizes on various benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make Large Multimodal Models better reason and think. They use something called self-evolving training, where the model learns from its own mistakes. The authors figure out what makes this work best, including things like how you train the model, what rewards it gets, and how you ask it questions. They even look at what happens when the model is learning and how to make it fair. This all leads to a plan called MSTaR that helps models of different sizes do better on tests. |
Keywords
» Artificial intelligence » Prompt
