Reward Design for Physical Reasoning in Vision-Language Models

1. 1Apply Supervised Fine-Tuning (SFT) to Vision-Language Models to improve physical reasoning
2. 2Use Group Relative Policy Optimization (GRPO) to fine-tune models and enhance reasoning gains
3. 3Design reward functions that integrate visual perception, domain knowledge, and multi-step symbolic inference
4. 4Evaluate models on physics benchmarks to assess physical reasoning capabilities
5. 5Fine-tune models using reward design and evaluate performance on downstream tasks

Researchers and engineers working on Vision-Language Models can benefit from this knowledge to improve their models' physical reasoning capabilities

💡 Reward design plays a crucial role in improving physical reasoning in Vision-Language Models

💡 Improve physical reasoning in Vision-Language Models using reward design and fine-tuning!