Unveiling Deep Seek R1: Reinforcement Learning Revolution
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In this riveting episode by 1littlecoder, the team delves into the groundbreaking creation of the Deep Seek R1 model, a true game-changer in the realm of language models. Bucking the trend of traditional pre-training methods, Deep Seek R1 takes a bold leap by focusing solely on post-training, setting it apart from its predecessors. Powered by the robust Deep Seek V3 base model, a cutting-edge mixture of experts model, Deep Seek R1 harnesses the power of reinforcement learning, specifically the grpo algorithm, to push the boundaries of language model development.
With a daring 10,000 reinforcement learning steps, the team meticulously crafted Deep Seek R1, surpassing the performance of the earlier Deep Seek R10 model in key benchmarks. While Deep Seek R10 showcased exceptional reasoning abilities, it grappled with issues like language inconsistency and readability, prompting the evolution into the more refined Deep Seek R1. By incorporating cold start data, supervised fine-tuning, and reinforcement learning techniques, Deep Seek R1 emerged as a formidable contender in the competitive landscape of language models.
Not stopping at Deep Seek R1, the team embarked on a journey to distill the model's prowess into smaller, more efficient versions. Through a meticulous distillation process, they birthed a range of distilled models based on Deep Seek R1, demonstrating superior performance despite their reduced parameter counts. This innovative approach underscores the team's commitment to pushing the boundaries of language model development, showcasing the transformative power of reinforcement learning and distillation techniques in shaping the future of AI technology.
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
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Positive feedback on the video and appreciation for making AI concepts easier to understand
Requests for high-resolution images and sharing of specific models (Kimi model, TinyZero LLM training process)
Technical questions and discussions on model comparisons and training processes
Suggestions for improvement such as fixing microphone clipping and exposure
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