Walk me through the training pipeline for a model like...

The training pipeline for DeepSeek R1 consists of five distinct stages. First, begin with a strong pretrained base model. Second, perform Supervised Fine-Tuning (SFT) on long Chain-of-Thought (CoT) data to establish a reasoning format. Third, apply Group Relative Policy Optimization (GRPO) RL using a combination of binary accuracy, format, and language consistency rewards on verifiable tasks. Fourth, conduct post-training SFT that combines reasoning and general data. Finally, perform RLHF using GRPO to optimize for instruction following, ensuring the RL stage is treated as distinct from the final RLHF stage.