DeepSeek has introduced its first-generation models, the DeepSeek-R1 and DeepSeek-R1-Zero, designed to address intricate reasoning tasks. The DeepSeek-R1-Zero model is notable for being trained exclusively through large-scale reinforcement learning (RL), bypassing the need for supervised fine-tuning (SFT) initially. This innovative training method has reportedly led to the emergence of various potent reasoning behaviors, including self-verification, reflection, and extended chains of thought. Researchers at DeepSeek indicate that this is the first open research to confirm that reasoning capabilities in large language models (LLMs) can be effectively incentivized solely through RL.
Despite these advancements, DeepSeek-R1-Zero is not without its limitations, facing challenges such as repetitive outputs, poor readability, and language mixing. To combat these issues, DeepSeek developed the DeepSeek-R1 model, which incorporates cold-start data prior to RL training to enhance reasoning capabilities. The performance of DeepSeek-R1 is competitive with OpenAI’s acclaimed o1 system, particularly in mathematics, coding, and general reasoning tasks. Both DeepSeek-R1-Zero and DeepSeek-R1 are open-source, alongside smaller distilled models.
Notably, the DeepSeek-R1-Distill-Qwen-32B has outperformed OpenAI’s o1-mini in various benchmarks, achieving impressive scores in categories like MATH-500 and AIME 2024. DeepSeek’s development pipeline combines SFT and RL, allowing budding researchers to build on strong foundational reasoning. Moreover, the importance of distillation is highlighted, as it enables the transfer of reasoning abilities from bigger models to smaller, efficient versions, enhancing overall performance across various configurations. DeepSeek has adopted the MIT License for its models, allowing for commercial use and modifications, thus fostering innovation within the AI community.