How DeepSeek-R1 Was Built, for Dummies
DeepSeek launched DeepSeek-R1, a reasoning model trained with pure reinforcement learning, achieving performance comparable to OpenAI's o1. It features a cost-effective API and highlights open-source potential in AI.
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DeepSeek has introduced a new reasoning model, DeepSeek-R1, which demonstrates that it is possible to train a model to achieve performance comparable to OpenAI's o1 using pure reinforcement learning (RL) without labeled data. This model, known as DeepSeek-R1-Zero, was initially trained using a pure-RL approach, which, while effective, faced challenges such as poor readability. To address these issues, DeepSeek-R1 underwent a multi-stage training process that included supervised fine-tuning and rejection sampling to enhance its reasoning capabilities. The model achieved impressive results, including an 86.7% pass rate in a prestigious mathematics competition, matching OpenAI's performance. The training utilized the Group Relative Policy Optimization (GRPO) framework, which allows the model to learn from predefined scoring rules rather than relying on a critic model. DeepSeek's open-source approach contrasts with OpenAI's more secretive methods, earning praise from the AI community. The DeepSeek-R1 model is available for use through a cost-effective API, offering a maximum context length of 64K tokens, although it lacks some advanced features found in OpenAI's offerings. The development of DeepSeek-R1 highlights the potential for open-source models to compete with proprietary systems in the AI landscape.
- DeepSeek-R1 matches OpenAI's o1 performance using pure reinforcement learning.
- The model was trained through a multi-stage process to improve readability and reasoning.
- DeepSeek's open-source approach contrasts with OpenAI's secretive methods.
- The model is available via a cost-effective API, making it accessible for developers.
- DeepSeek-R1 demonstrates the potential of open-source models in the AI industry.
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