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DeepSeek-R1: Reasoning LLMs via Reinforcement Learning
We talk about DeepSeek-R1, a novel language model with enhanced reasoning capabilities achieved through reinforcement learning (RL). The researchers explored training methodologies, including DeepSeek-R1-Zero which uniquely utilizes large-scale RL without initial supervised fine-tuning (SFT), demonstrating emergent reasoning behaviors. To improve readability and further boost performance, DeepSeek-R1 incorporates a multi-stage training process with cold-start data before RL and achieves results comparable to OpenAI's o1-1217 on reasoning tasks. Furthermore, the paper discusses the distillation of DeepSeek-R1's reasoning abilities into smaller, more efficient models, showcasing their strong performance on various benchmarks.
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By Claude Henri NguembiWe talk about DeepSeek-R1, a novel language model with enhanced reasoning capabilities achieved through reinforcement learning (RL). The researchers explored training methodologies, including DeepSeek-R1-Zero which uniquely utilizes large-scale RL without initial supervised fine-tuning (SFT), demonstrating emergent reasoning behaviors. To improve readability and further boost performance, DeepSeek-R1 incorporates a multi-stage training process with cold-start data before RL and achieves results comparable to OpenAI's o1-1217 on reasoning tasks. Furthermore, the paper discusses the distillation of DeepSeek-R1's reasoning abilities into smaller, more efficient models, showcasing their strong performance on various benchmarks.