DeepSeek-R1 Models Compete with OpenAI in Effectiveness

DeepSeek has launched its inaugural DeepSeek-R1 and DeepSeek-R1-Zero models, crafted to address complex reasoning tasks efficiently.

The DeepSeek-R1-Zero model has been developed using large-scale reinforcement learning (RL) alone, bypassing the need for supervised fine-tuning (SFT) as a preliminary step. DeepSeek asserts that this strategy has fostered the natural development of several compelling reasoning behaviors, including self-verification, reflection, and generating extensive thought processes.

Researchers at DeepSeek noted that DeepSeek-R1-Zero is pioneering open research that demonstrates large language models (LLMs) can enhance their reasoning abilities strictly through RL, eliminating the necessity for SFT. This achievement highlights the model's novel underpinnings and opens new avenues for RL-centered enhancements in reasoning AI.

However, the capabilities of DeepSeek-R1-Zero aren't without their challenges, such as repetitive outputs, readability issues, and language mixing, which could create obstacles in practical applications. To counter these issues, DeepSeek has introduced its refined model: DeepSeek-R1.

Building upon the earlier version, DeepSeek-R1 incorporates cold-start data before RL training. This additional pre-training enhances the model's reasoning capabilities and addresses many of the limitations noted in DeepSeek-R1-Zero.

DeepSeek-R1 notably achieves a performance level similar to OpenAI's highly-acclaimed o1 system across math, coding, and general reasoning tasks, reinforcing its status as a formidable competitor.

In a move to encourage further development, DeepSeek has open-sourced both DeepSeek-R1-Zero and DeepSeek-R1, along with six smaller distilled models. Among these, DeepSeek-R1-Distill-Qwen-32B has surpassed even OpenAI's o1-mini in multiple benchmarks.

🚀 DeepSeek-R1 is now available!

⚡ On par with OpenAI-o1
📖 Fully open-source model & technical report
🏆 Licensed under MIT: Open to distillation and commercialization!

🌐 Visit their website & API today!

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DeepSeek has provided a glimpse into its rigorous development pipeline for reasoning models, which synergizes supervised fine-tuning with reinforcement learning techniques.

The company mentions that the process comprises two SFT phases that establish core reasoning and non-reasoning skills, followed by two RL phases focused on discovering complex reasoning patterns and aligning these abilities with human preferences.

DeepSeek expressed confidence in their process, suggesting it will contribute to creating superior models within the AI sector, potentially catalyzing future innovations.

One major accomplishment of their RL-centric approach is DeepSeek-R1-Zero's proficiency in executing sophisticated reasoning patterns without initial human guidance, marking a novelty within the open-source AI research community.

Researchers at DeepSeek also emphasized the value of distillation—the technique of transferring reasoning skills from larger models to smaller, more efficient ones, unlocking performance improvements even in smaller setups.

Smaller distilled versions of DeepSeek-R1—such as the 1.5B, 7B, and 14B editions—have demonstrated competence in specialized applications. The distilled models have surpassed results achieved via RL training on models of similar sizes.

🔥 Bonus: Open-Source Distilled Models!

🔬 Derived from DeepSeek-R1, 6 small models fully open-sourced
📊 32B & 70B models competitive with OpenAI-o1-mini
🤝 Empowering the open-source community

🌍 Advancing the frontiers of open AI!

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Researchers have access to these distilled models in configurations ranging from 1.5 billion to 70 billion parameters, compatible with Qwen2.5 and Llama3 architectures. This adaptability facilitates diverse uses across tasks from coding to natural language processing.

DeepSeek has applied the MIT License to its repository and weights, granting permissions for commercial applications and downstream modifications. Derived works, like employing DeepSeek-R1 for training other large language models (LLMs), are allowed. Nevertheless, users must ensure compliance with the licenses of the original base models, such as Apache 2.0 and Llama3 licenses.