The training data for DeepSeek-R1 and Llama 3.1 exhibit several key differences, reflecting distinct approaches to model development.
DeepSeek-R1 Training Data
DeepSeek-R1 is trained using a multi-stage process that combines reinforcement learning (RL) with supervised fine-tuning (SFT). The model starts with a "cold start" phase, where it is fine-tuned on a small set of carefully crafted examples to improve clarity and readability. This is followed by pure RL to enhance reasoning skills, similar to R1-Zero. Near RL convergence, the model uses rejection sampling to create synthetic data by selecting the best examples from previous RL runs. This synthetic data is then merged with supervised data from DeepSeek-V3-Base in domains like writing, factual QA, and self-cognition. The final stage involves another round of RL across diverse prompts and scenarios to further generalize the model's capabilities[1][4].
Llama 3.1 Training Data
Llama 3.1, on the other hand, is trained on a massive corpus of approximately 15 trillion tokens from publicly available sources, with a knowledge cut-off date of December 2023[8]. The training dataset includes a balanced mix of general domains, mathematical and reasoning data, multilingual texts, and code from various programming languages to enhance code generation and understanding capabilities[5]. The model undergoes initial pre-training using a next-token prediction objective, followed by long-context pre-training to handle long documents and complex reasoning tasks. The data mix is carefully adjusted to improve performance on specific tasks, such as increasing non-English data for multilingual capabilities and up-sampling mathematical data for better reasoning[2][5].
Key Differences
1. Training Approach: DeepSeek-R1 relies heavily on reinforcement learning and synthetic data generation, while Llama 3.1 uses a more traditional supervised learning approach with a massive pre-training dataset.
2. Data Sources: DeepSeek-R1 uses a combination of initial cold-start data and synthetic data generated during the RL process. In contrast, Llama 3.1 is trained on a large corpus of publicly available data.
3. Data Volume and Quality: Llama 3.1 is trained on a much larger dataset (~15 trillion tokens) compared to the relatively small initial dataset used for DeepSeek-R1. However, DeepSeek-R1's use of synthetic data allows it to achieve high performance in reasoning tasks despite the smaller initial dataset.
4. Focus Areas: Both models focus on improving reasoning and knowledge capabilities, but DeepSeek-R1 places a strong emphasis on reasoning through RL, while Llama 3.1 also focuses on multilingual and coding capabilities.
Overall, the training data for DeepSeek-R1 and Llama 3.1 reflect different strategies in model development, with DeepSeek-R1 leveraging RL and synthetic data to achieve strong reasoning capabilities and Llama 3.1 relying on a large-scale supervised learning approach to cover a broad range of tasks.
Citations:
[1] https://www.vellum.ai/blog/the-training-of-deepseek-r1-and-ways-to-use-it
[2] https://www.linkedin.com/pulse/dissecting-llama-31-deep-dive-benedict-smith-agi6f
[3] https://docsbot.ai/models/compare/deepseek-r1/llama-3-1-8b-instruct
[4] https://huggingface.co/blog/open-r1
[5] https://kili-technology.com/large-language-models-llms/llama-3-1-guide-what-to-know-about-meta-s-new-405b-model-and-its-data
[6] https://docsbot.ai/models/compare/deepseek-r1/llama-3-1-405b-instruct
[7] https://www.datacamp.com/tutorial/fine-tuning-deepseek-r1-reasoning-model
[8] https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct
[9] https://www.prompthub.us/blog/deepseek-r-1-model-overview-and-how-it-ranks-against-openais-o1