Correcting What You Cannot See: Credit Assignment for Perception Distillation in Multimodal Reasoners
Researchers introduce Perception-Correction Distillation, a label-free method that distinguishes between perception and reasoning failures in multimodal models to improve training efficiency.
- Introduces Perception-Correction Distillation (PCD) to isolate perception errors from reasoning errors.
- Uses teacher-student disagreement and downstream failures as signals without needing extra labels.
- Improves on-policy distillation by providing dense supervision specifically for correctable visual mistakes.
Training multimodal models is difficult because a wrong answer can stem from bad visual perception or flawed reasoning logic. Existing metrics like Perception Success Rate struggle to separate these two issues, leading to ambiguous feedback during the distillation process. This paper introduces Perception-Correction Distillation, a label-free approach designed to solve this credit assignment problem. The method identifies correctable perception failures by analyzing downstream failures and disagreements between teacher and student models. This allows the system to provide targeted supervision for visual understanding, improving the overall robustness of multimodal reasoners without requiring additional human labels.
Offers a new technique to refine vision-language models by pinpointing exactly where visual processing fails.
Leads to more reliable multimodal AI products by reducing errors caused by poor visual understanding.
- Credit Assignment
- The process of determining which parts of a model or sequence of actions are responsible for a specific outcome.
- Distillation
- A technique where a smaller student model is trained to mimic the behavior of a larger teacher model.
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