Post-Training & Alignment Expertise: Hands-on experience designing post-training and student-conversion pipelines for generative models-including few-step diffusion/flow distillation (e.g., DMD, MMD, Consistency Models, Rectified Flow), autoregressive exposure-bias mitigation (e.g., Self-Forcing), SFT, and Reinforcement Learning (RLAIF / reward-model alignment)-to control and stabilize long-horizon rollouts. Drive Advanced Post-Training & Alignment: Design and implement robust post-training paradigms, including Supervised Fine-Tuning (SFT), Reinforcement Learning with AI/Critic Feedback (RLAIF), model merging/souping, and 3D/semantic conditioning to guarantee strict physical realism, counterfactual pose-divergence stability, and fine-grained control over safety-critical scene elements (signage, traffic signals, VRU gestures, and adverse weather).