Beyond Exact Match: Task-Aware GRPO for Cross-Domain PCBA Visual Question Answering

2026-09-17

AI Summary

本文针对PCBA视觉问答中域迁移及输出空间异质性问题，提出了一种多模态推理框架及任务感知的GRPO方法，通过设计特定奖励机制与鲁棒推断策略有效解决了上述挑战。

Abstract

In automated Printed Circuit Board Assembly (PCBA) inspection, standards-guided decisions require systems to jointly reason over fine-grained visual cues, component semantics, and manufacturing knowledge. Although large vision-language models (VLMs) provide a promising foundation, their deployment is hindered by the domain shift between standards-derived samples and real-world production-line imagery, together with heterogeneous output spaces spanning choice-based and numerical counting tasks. To address these challenges, we propose a multimodal reasoning framework for cross-domain PCBA visual question answering. The framework converts standards-derived, real-world, and auxiliary PCB-domain data into a unified instruction format and constructs verified reasoning traces aligned with visual evidence, question semantics, candidate options, and ground-truth answers. We further introduce Task-Aware Group Relative Policy Optimization (GRPO), which moves beyond exact-match supervision by integrating multi-component semantic rewards for choice-based questions, distance-aware rewards for counting questions, and an auxiliary format reward for valid outputs. During inference, answer-option semantic consistency correction, self-consistency voting, and multi-model arbitration are combined to improve prediction robustness. The proposed system achieves an Overall Score of 83.24 on the official PCBA Standard-to-Real Grand Challenge leaderboard, demonstrating the effectiveness of task-aware reward design and robust inference for cross-domain PCBA visual question answering.

Problem

• automated PCBA inspection
• domain shift
• heterogeneous output spaces

Innovation

• Task-Aware GRPO
• Cross-Domain PCBA VQA
• Unified Instruction Format
• Semantic Rewards
• Robust Inference