Everyone’s talking about DeepSeek-R1, a new model that uses chain-of-thought to generate responses that rival OpenAI’s o1 at a fraction of the cost. Last week, we explained its training and architecture in depth at The Batch: https://hubs.la/Q03476Yx0
AI Research Engineer | Building foundational models | Advancing model architectures & efficiency
3dDeepSeek-R1 is making waves for a reason! The combination of Mixture-of-Experts architecture, chain-of-thought reasoning without explicit prompting, and reinforcement learning techniques like group relative policy optimization is genuinely exciting. 🚀 I find it fascinating how DeepSeek-R1 not only handles reasoning transparently with <think> tags but also doubles as a teacher for distillation, enabling smaller models to inherit some of its reasoning capabilities. This open approach feels like a step forward for both accessibility and innovation in AI. Given its significantly lower cost compared to o1, DeepSeek's approach could make it more accessible for applications requiring frequent, large-scale reasoning, like academic research or budget-conscious startups. It’s a fascinating tradeoff between investing heavily upfront (DeepSeek's RL training approach) vs. paying as you go (o1's inference optimization approach)