This podcast episode explores the groundbreaking research behind DeepSeek-R1, a state-of-the-art, open-source reasoning model. The episode delves into how DeepSeek-R1 is trained using large-scale reinforcement learning techniques. It explains the key differences between DeepSeek-R1 and DeepSeek-R1-Zero, highlighting that DeepSeek-R1-Zero is trained without supervised fine-tuning.
Key topics covered include:
• The Group Relative Policy Optimization (GRPO) method, a rule-based reinforcement learning approach used by DeepSeek. This method uses accuracy and format rewards.
• The self-evolution process of DeepSeek-R1-Zero, where the model learns to allocate more thinking time for reasoning tasks.
• The "Aha moment" phenomenon, where DeepSeek-R1-Zero reevaluates and corrects its reasoning.
• The multi-stage training pipeline of DeepSeek-R1, which includes cold-start, reasoning reinforcement learning, rejection sampling, and diverse reinforcement learning.
• How DeepSeek-R1 addresses readability issues and language inconsistencies found in DeepSeek-R1-Zero.
• The impressive performance of DeepSeek-R1, which is comparable to or surpasses OpenAI's o1 model on various benchmarks.
• The distillation of DeepSeek-R1 into smaller models with high reasoning capabilities.
This episode also touches on the DeepSeek team's unsuccessful attempts using process reward models and Monte Carlo Tree Search, and how these experiences helped refine their current approach. Finally, the podcast underscores the importance of reinforcement learning in enhancing model reasoning capabilities, showing how DeepSeek-R1 demonstrates a significant advancement in the field
Content Source:
www.github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf
www.aipapersacademy.com/deepseek-r1/
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