DeepSeek-R1 had an RLHF step in their post-training pipeline (section 2.3.4 of their technical report[1]). In addition, the "reasoning-oriented reinforcement l

DeepSeek-R1 had an RLHF step in their post-training pipeline (section 2.3.4 of their technical report[1]). In addition, the "reasoning-oriented reinforcement learning" step (section 2.3.2) used an approach that is almost identical to RLHF in theory and implementation. The main difference is that they used a rule-based reward system, rather than a model trained on human preference data. If you want to train a model like DeepSeek-R1, you'll need to know the fundamentals of reinforcement learning on language models, including RLHF. [1] https://arxiv.org/pdf/2501.12948 https://arxiv.org/pdf/2501.12948