Official Alimama tech channel, showcasing all of Alimama's technical innovations.
The article details the development and deployment of LORE, a large generative model that reshapes e‑commerce search relevance by combining knowledge injection, chain‑of‑thought reasoning, and multimodal alignment, achieving simultaneous improvements in user experience and revenue metrics.
Alibaba’s open‑source ROCK environment sandbox and the ROLL reinforcement‑learning engine together provide a standardized, high‑throughput training loop that lets developers scale Agentic AI from a single machine to thousands of parallel instances while simplifying debugging and resource management.
This article introduces the 3A collaborative framework—Async architecture, Asymmetric PPO mini‑critics, and an attention‑based reasoning rhythm—demonstrating how decoupled, fine‑grained parallel training and structure‑aware reward allocation dramatically improve efficiency, scalability, and interpretability of reinforcement learning for large language models.
The award‑winning industrial graph learning system developed by Peking University and Alibaba Mama combines novel dynamic graph embedding and GNN techniques, scales to millions of merchants, and has driven over 12% ad ROI improvement while publishing dozens of top‑conference papers.
EMNLP 2025 in Suzhou showcases Taobao's booth featuring four cutting‑edge AI papers that introduce a novel embedding compression framework, an automatic iterative refinement method for complex instruction generation, a knowledge infusion scaling law for large language models, and a video caption optimization approach for text‑to‑video generation.
This article details Alibaba’s Taobao Star fashion AIGC model, explaining its data pipeline, captioning strategy, multi‑stage training, and impressive virtual try‑on results for users and merchants, while showcasing model‑based and model‑free generation and pose‑transfer capabilities.
Alibaba’s Secure Data Hub (SDH) leverages MPC, differential privacy and PPML to create a compliant data clean‑room for advertisers, allowing multi‑party data collaboration without exposing raw user data, thereby enhancing privacy, trust and efficiency in the advertising ecosystem.
Alibaba’s Taobao Starry Image Editing model tackles the e‑commerce challenge of maintaining visual consistency by introducing a high‑fidelity, plug‑in architecture, a million‑scale consistency dataset, and multi‑stage multilingual training, enabling precise, controllable edits without altering product layout or background.
RecIS is an open‑source, PyTorch‑based unified framework designed for ultra‑large‑scale sparse‑dense computation in recommendation systems, offering a full solution for training models with massive samples, multimodal inputs, and large embeddings, and demonstrating significant performance gains over TensorFlow and TorchRec in production deployments.
This article provides a comprehensive overview of differential privacy, covering its mathematical foundations, evolution from theory to engineering, classification of privacy mechanisms, practical implementation cases such as Alibaba's Secure Data Hub, and diverse application scenarios across healthcare, finance, location analytics, and energy forecasting.
