Large language models often exhibit selection bias in multiple-choice and pairwise evaluation tasks due to non-semantic factors such as option positions and label symbols. Existing inference-time debiasing methods are computationally expensive and may harm reasoning capabilities, while pointwise training approaches ignore the importance of consistent answers across different question permutations. This paper proposes Permutation-Aware Group Relative Policy Optimization (PA-GRPO), which addresses selection bias by enforcing permutation-consistent semantic reasoning through two complementary mechanisms: cross-permutation advantage computation relative to the mean reward over all permutations, and consistency-aware reward that encourages stable decision-making across different permutations. The proposed approach effectively mitigates selection bias while preserving the model's reasoning capabilities.