Advancements in AI Reasoning Models
DeepSeek-AI has unveiled its first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1, which utilize reinforcement learning to significantly improve the reasoning capabilities of large language models (LLMs). The DeepSeek-R1-Zero model optimizes reasoning performance without relying on supervised data, achieving a remarkable pass rate of 71.0% on the AIME 2024 benchmark.
Meanwhile, DeepSeek-R1 incorporates cold-start data to ensure coherent and user-friendly outputs, with the 14B DeepSeek-R1-Distill-Qwen-32B model reaching a 72.6% pass rate. These models, available under the MIT License, promise enhanced multilingual support and efficient software engineering capabilities.
As competition in the AI landscape intensifies, DeepSeek's offerings suggest a growing ability for Chinese AI labs to match and potentially surpass established players like OpenAI.
The press radar on this topic:
DeepSeek's latest R1-Zero model matches OpenAI's o1 in reasoning benchmarks
DeepSeek-AI Releases DeepSeek-R1-Zero and DeepSeek-R1: First-Generation Reasoning Models that Incentivize Reasoning Capability in LLMs via Reinforcement Learning - MarkTechPost
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