Research note: Model specifications and benchmark results are time-bound. Check the dated primary sources below before using them for a technical or purchasing decision.

The release of DeepSeek V4 solidified the post-RLHF revolution. By relying on Pure Reinforcement Learning (RL) directly from rule-based reward functions (unit test outcomes, mathematical correctness) without human demonstration datasets (SFT), DeepSeek demonstrated that complex reasoning behaviors emerge organically.

The Pure RL Breakthrough: Models trained with pure RL discover novel search heuristics, self-reflection steps, and error-correction strategies that human annotators never conceived of.

Sources, Whitepapers & Further Reading

  • • DeepSeek-R1 Technical Report: DeepSeek-AI. (2025). DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning. arXiv:2501.12948.
  • • Group Relative Policy Optimization (GRPO): Shao, Z., et al. (2024). DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models. arXiv:2402.03300.