DeepSeek has launched its R1 series of large language models, the R1-Zero and R1, which utilize a Mixture of Experts architecture to enhance reasoning tasks while reducing inference costs. Specifically, only 10% of their 671 billion parameters are activated per query. While R1-Zero employs a novel training method that skips supervised fine-tuning, it struggles with output quality. In contrast, the R1 model incorporates this fine-tuning to enhance performance while remaining competitive with OpenAI's o1-LLM across various benchmarks. DeepSeek has also introduced smaller models for increased hardware efficiency.
DeepSeek's new R1 series significantly enhances reasoning model performance, utilizing a unique Mixture of Experts approach to optimize resource allocation and reduce inference costs.
The R1-Zero model showcases an innovative training method by bypassing traditional supervised fine-tuning, yet still exhibits some reasoning capabilities, though with quality issues.
The refined R1 model incorporates supervised fine-tuning, resulting in improved output quality, allowing it to compete closely with OpenAI's o1-LLM in performance benchmarks.
DeepSeek's adoption of a Mixture of Experts architecture enables the R1 series to activate less than 10% of its 671 billion parameters for specific queries.
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