The training data and methods for DeepSeek-R1 and GPT-4o-0513 differ significantly in several aspects:
DeepSeek-R1 Training Data and Methods
1. Focus on Reasoning: DeepSeek-R1 is primarily a reasoning model that uses reinforcement learning (RL) to enhance its reasoning capabilities. It starts with a base model, DeepSeek-V3, which is fine-tuned using thousands of high-quality examples to improve clarity and readability[1][4].
2. Multi-Stage Training Process: The model undergoes a multi-stage training process:
- Initial Fine-Tuning: It begins with supervised fine-tuning on a small dataset to establish a structured foundation.
- Pure Reinforcement Learning: This is followed by pure RL to develop reasoning skills without human supervision.
- Rejection Sampling: The model generates synthetic data by selecting the best examples from previous RL runs, which are then merged with supervised data.
- Final RL Stage: The model undergoes another round of RL across diverse prompts to enhance generalization[1][3].
3. Language Focus: DeepSeek-R1 Lite is particularly optimized for Chinese language materials and specific professional fields, with meticulous data filtering and over-sampling[3].
GPT-4o-0513 Training Data and Methods
1. Multimodal Capabilities: GPT-4o is trained on a diverse dataset that includes a large amount of multi-language text, with a significant proportion of English data. It supports multi-modal inputs such as text, images, and audio[2][3].
2. Training Methods: GPT-4o employs supervised fine-tuning, multi-stage reinforcement learning (RLHF), and multi-modal alignment. This allows it to understand the relationships between different forms of information, such as aligning text descriptions with images[2][3].
3. Large-Scale Data: The model is trained using large-scale, high-quality multi-modal datasets to enhance its natural language processing and multi-modal interaction capabilities. It uses an end-to-end training method to uniformly train different modalities of data[2][3].
4. Probabilistic Generation: Unlike DeepSeek-R1, GPT-4o is a probabilistic generation model based on the Transformer architecture. It generates text by predicting the probability distribution of the next word or character, ensuring coherence and reasonableness[3].
In summary, DeepSeek-R1 focuses on reasoning tasks using reinforcement learning and is optimized for specific languages and domains, while GPT-4o emphasizes comprehensive multimodal capabilities and is trained on a broader range of data types and languages.
Citations:
[1] https://www.vellum.ai/blog/the-training-of-deepseek-r1-and-ways-to-use-it
[2] https://www.techtarget.com/whatis/feature/GPT-4o-explained-Everything-you-need-to-know
[3] https://cciedump.spoto.net/newblog/difference-between-deepseek-r1-and-gpt-4o:-underlying-principles-and-parameter.html
[4] https://huggingface.co/blog/open-r1
[5] https://klu.ai/glossary/openai-gpt-4-omni
[6] https://docsbot.ai/models/compare/gpt-4o/deepseek-r1
[7] https://www.datacamp.com/tutorial/fine-tuning-deepseek-r1-reasoning-model
[8] https://openai.com/index/hello-gpt-4o/