Detached Skip-Links and $R$-Probe: Decoupling Feature Aggregation from Gradient Propagation for MLLM OCR

1. 1Identify optimization issues in multi-layer feature fusion
2. 2Decouple feature aggregation from gradient propagation using Detached Skip-Links
3. 3Implement $R$-Probe to mitigate gradient interference
4. 4Evaluate and refine the approach for improved MLLM OCR performance

ML researchers and engineers working on MLLMs can benefit from this approach to improve model performance on OCR tasks, and software engineers can apply these principles to optimize model training

💡 Detached Skip-Links and $R$-Probe can mitigate gradient interference and improve model stability

💡 Decoupling feature aggregation from gradient propagation improves MLLM OCR performance