Solving Real-World AI Challenges at JD Retail: Reward Model Ensembles, Query Expansion, and Model Pruning
This article recounts how JD Retail's young algorithm engineers tackled diverse AI problems—optimizing reward‑model ensembles for ad image generation, building large‑language‑model‑based query expansion, and pruning diffusion models with FFT and RDP—while sharing their technical approaches, code snippets, and growth reflections.