Balancing Perception and Reasoning in Vision-Language Models

Vision-language models have made strides in reasoning but still lag in perception. This gap limits end-to-end visual reasoning capabilities. A recent study offers insights into this issue, introducing a diagnostic framework with synthetic tasks to separate perception from reasoning.

The Perception-Reasoning Gap

Analysis reveals a consistent asymmetry: post-training enhances reasoning more than perception. Notably, this mechanism varies by training type. In supervised fine-tuning (SFT), the imbalance arises from token allocation in chain-of-thought supervision. Perception gets fewer tokens, thus a weaker training signal. By dynamically reweighting the loss, researchers boosted end-to-end performance by up to 18.2%.

Reinforcement Learning Challenges

For reinforcement learning (RL), the issue is different. Here, reward coupling skews towards reasoning, weakening perception learning signals. Introducing a perception-aware reward mitigates this. It enhances accuracy by up to 6.0%, even when groundtruth perception rewards aren't available. A surrogate reward still provides a useful signal, offering a 3.2-point gain.

Implications for Model Training

These findings are essential for advancing AI systems. If perception continues to lag, models won't perform optimally in real-world applications. Researchers suggest that balancing perception and reasoning could drastically improve model outputs. But how long will it take for these interventions to become standard practice?

The study's key contribution: a comprehensive diagnosis of asymmetric optimization and actionable strategies to address it. As ML systems grow in complexity, focusing on perception shouldn't be an afterthought. Better balance can lead to more reliable and versatile AI models.