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This primer dissects the innovative architecture behind DeepSeek-R1 covering the following key aspects: 🔹 Architectural Foundations - Mixture of Experts (MoE) - Multihead Latent Attention (MLA) - FP8 Quantization - Multi-Token Prediction (MTP) 🔹 Training Pipeline: from Pre-Training to Reasoning - Stage 1: Cold Start with Supervised Fine-Tuning (SFT) - Stage 2: Reinforcement Learning (RL); Rewards 🔹 Group Relative Policy Optimization (GRPO) - How GRPO Works - Step-by-Step Breakdown - PPO vs. DPO vs. KTO vs. APO vs. GRPO 🔹 Emergent Reasoning Behaviors 🔹 Distillation: Reasoning in Compact Models 🔹 Results 🔹 Open Questions 🔹 Open-R1 - Objectives of Open-R1 - Impact on the Community