SQL code completion boosts developer productivity by predicting missing snippets from editing SQL contexts. However, existing solutions yield low acceptance since they cannot abstain when no suggestion is needed and lack SQL-specific training. To address both limitations, we introduce PACE-SQL, a preference-aligned framework that improves SQL completion accuracy and user preference alignment. We build two datasets with completion and abstention samples: WeSQL-1.5M from real-world interaction logs to capture authentic user preferences, and OpenSQL-FIM from public SQL corpora. We adopt continual pretraining and supervised fine-tuning to improve completion quality, and design a reward function in the reinforcement learning to enhance alignment with user preferences. Experiments show PACE-SQL improves Exact Match and Edit Similarity scores by 9.67% and 7.52% over the best baseline on WeSQL-1.5M and raises online user acceptance rate from 5% to 29%.

Due to the success of large-scale visual-language pretraining (VLP) models and the widespread use of image-text retrieval in industry areas, it is now critically necessary to reduce the model size and streamline their mobile-device deployment. Single- and dual-stream model structures are commonly used in image-text retrieval with the goal of closing the semantic gap between textual and visual modalities. While single-stream models use deep feature fusion to achieve more accurate cross-model alignment, dual-stream models are better at offline indexing and fast inference. We propose a Multi-teacher Cross-modality Alignment Distillation (MCAD) technique to integrate the advantages of single- and dual-stream models. By incorporating the fused single-stream features into the image and text features of the dual-stream model, we formulate new modified teacher similarity distributions and features. Then, we conduct both distribution and feature distillation to boost the capability of the student dual-stream model, achieving high retrieval performance without increasing inference complexity. Extensive experiments demonstrate the remarkable performance and high efficiency of MCAD on image-text retrieval tasks. Furthermore, we implement a lightweight CLIP model on Snapdragon/Dimensity chips with only ~100M running memory and ~8.0ms search latency, achieving the mobile-device application of VLP models.