DeepSeek vs. ChatGPT: prospects and challenges.

Area of Science:

• Artificial Intelligence
• Machine Learning

Background:

• OpenAI's ChatGPT dominates general AI tasks.
• DeepSeek introduces DeepSeek-R1 as an open-source alternative.
• Existing models often require extensive supervised fine-tuning (SFT).

Purpose of the Study:

• Analyze the architecture and performance of DeepSeek-R1.
• Compare DeepSeek-R1's efficiency and capabilities against ChatGPT.
• Investigate the effectiveness of rule-based reinforcement learning (RL) without SFT.

Main Methods:

• DeepSeek-R1 architecture analysis.
• Rule-based reinforcement learning (RL) without preliminary supervised fine-tuning (SFT).
• Multi-stage training with cold-start data preceding RL.
• Reward modeling for optimizing the training process.

Main Results:

• DeepSeek-R1 demonstrates high efficiency by omitting preliminary SFT.
• The model achieves significant performance in technical and reasoning tasks.
• DeepSeek-R1's open-source nature allows for transparent decision-making processes.
• ChatGPT excels in general tasks and creative applications.

Conclusions:

• DeepSeek-R1 presents a viable, efficient open-source alternative for specific AI applications.
• Future AI development requires addressing data quality, privacy, and ethical considerations.
• Advancements are expected in multi-modal capabilities and handling larger datasets.