Dynamic binary translation (DBT) is a key system technology that enables many important system applications such as system virtualization and emulation. To achieve good performance, it is important for a DBT system to be equipped with high-quality translation rules. However, most translation rules in existing DBT systems are created manually with high engineering efforts and poor quality. To solve this problem, a learning-based approach was recently proposed to automatically learn semantically-equivalent translation rules, and symbolic verification is used to prove the semantic equivalence of such rules. But, they still suffer from some shortcomings.