Description
DeepSeek-R1 demonstrates that large language models can be trained to develop advanced reasoning abilities using reinforcement learning (RL) rather than depending on human-annotated reasoning traces. By building on DeepSeek-V3 Base and using a method called Group Relative Policy Optimization (GRPO), the authors use only final prediction correctness as the reward signal—without instructing the specific reasoning path. The result is that the model learns emergent behaviors like self-reflection, verification steps, and dynamic adaptation of strategy. DeepSeek-R1 outperforms models trained with supervised learning using human demonstrations in tasks involving mathematics, coding, and STEM reasoning. The supplementary materials (ESM1) provide detailed experimental setups, dataset details (including how they handle decontamination of evaluation data), and additional evaluation metrics that support the main claims.
