📄 中文摘要
后训练技术使模型能够按照预期的方式运行。AMD人工智能副总裁Sharon Zhou在与Ben的对话中，强调了前沿实验室对这一技术的信心，但普通开发者仍在摸索后训练的内部机制及其重要性。Sharon详细解释了后训练如何优化模型的表现，使其更符合特定应用需求，并分享了在实际应用中遇到的挑战与解决方案。通过这一讨论，开发者可以更好地理解后训练的价值以及如何有效地应用这一技术。
现实世界中的生成性人工智能：Sharon Zhou谈后训练
出处: Generative AI in the Real World: Sharon Zhou on Post-Training
发布: 2026年3月12日
📄 English Summary
Generative AI in the Real World: Sharon Zhou on Post-Training
Post-training techniques enable models to operate as intended. In a conversation with Ben, Sharon Zhou, AMD's VP of AI, emphasizes the confidence that frontier labs have in this technology, while average developers are still exploring the underlying mechanics and significance of post-training. Sharon elaborates on how post-training optimizes model performance to meet specific application needs and shares challenges and solutions encountered in practical applications. This discussion helps developers better understand the value of post-training and how to effectively implement this technology.