Technical Strategy: Define the AIGC roadmap across pre-training and post-training workstreams — from distributed training infrastructure to alignment, evaluation, and inference deployment.
Pre-training & Infrastructure: Oversee the design of distributed training toolchains for ultra-large-scale AIGC models. Drive system-level optimization across computation, communication, and storage layers. Ensure training stability and efficiency at scale.
Post-training & Alignment: Guide architecture design for video generation post-training — including high-quality instruction data curation, preference alignment (RLHF, DPO, GRPO, PPO), and video quality enhancement pipelines.
Capability Expansion: Push the frontier on long-video modeling, storyline consistency, precise camera control, and multi-modal generation.
Evaluation & Quality: Establish video quality evaluation frameworks and multi-dimensional Reward Models to systematically measure and improve output quality.
Inference & Deployment: Drive model distillation, quantization, and inference acceleration to bring research models into production.
