Balanced Thinking: Improving Chain of Thought Training in Vision Language Models
#vision language models #chain of thought #training method #reasoning balance #AI performance
📌 Key Takeaways
- Researchers propose a new method to improve chain of thought training in vision language models.
- The approach aims to balance reasoning steps to enhance model performance on visual reasoning tasks.
- It addresses issues like overthinking or underthinking in multi-step reasoning processes.
- The method shows promising results in benchmarks, improving accuracy and efficiency.
📖 Full Retelling
arXiv:2603.18656v1 Announce Type: new
Abstract: Multimodal reasoning in vision-language models (VLMs) typically relies on a two-stage process: supervised fine-tuning (SFT) and reinforcement learning (RL). In standard SFT, all tokens contribute equally to the loss, even though reasoning data are inherently token-imbalanced. Long <think> traces overshadow short but task-critical <answer> segments, leading to verbose reasoning and inaccurate answers. We propose SCALe (Scheduled Curricu
🏷️ Themes
AI Training, Visual Reasoning
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Original Source
arXiv:2603.18656v1 Announce Type: new
Abstract: Multimodal reasoning in vision-language models (VLMs) typically relies on a two-stage process: supervised fine-tuning (SFT) and reinforcement learning (RL). In standard SFT, all tokens contribute equally to the loss, even though reasoning data are inherently token-imbalanced. Long <think> traces overshadow short but task-critical <answer> segments, leading to verbose reasoning and inaccurate answers. We propose SCALe (Scheduled Curricu
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