DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrated remarkable performance on reasoning.

DeepSeek-R1-Zero demonstrates capabilities such as self-verification, reflection, and generating long CoTs, marking a significant milestone for the research community. Notably, it is the first open research to validate that reasoning capabilities ...

January 22, 2025 - DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrates remarkable reasoning capabilities.

Last week, DeepSeek published their new R1-Zero and R1 “reasoner” systems that is competitive with OpenAI’s o1 system on ARC-AGI-1. R1-Zero, R1, and o1 (low compute) all score around 15-20% – in contrast to GPT-4o’s 5%, the pinnacle of years of pure LLM scaling.

January 20, 2025 - In section 2.2. DeepSeek-R1-Zero: ... we explore the potential of LLMs to develop reasoning capabilities without any supervised data, focusing on their self-evolution through a pure reinforcement learning process....

We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1.

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DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrated remarkable performance on reasoning.

Reinforcement Learning on the Base Model (DeepSeek-R1-Zero) DeepSeek-R1-Zero’s development hinged on Group Relative Policy Optimization (GRPO), a reinforcement learning (RL) framework designed for cost efficiency and effectiveness.

January 20, 2025 - DeepSeek has just released DeepSeek-R1 and DeepSeek-R1-Zero, two new reasoning models outperforming OpenAI-o1 on various benchmarks.

The result was R1, a model that not only keeps the reasoning power of R1-Zero but significantly improves accuracy, readability, and coherence. Unlike V3, which is optimized for general tasks, R1 is a true reasoning model. That means it doesn’t just give you an answer; it explains how it got there. Before responding, R1 generates a step-by-step chain of thought, making it especially useful for: ... According to the DeepSeek-R1 paper re-published in Nature and its supplementary information, R1’s training cost was the equivalent of just US$294K primarily on NVIDIA H800 chips.

February 12, 2025 - While I think this is an interesting hypothesis, I'm skeptical. You might be lowering the cost of your training corpus by a few million dollars, but I highly doubt you are getting novel, high quality data · We are currently in a world where SOTA base model seems to be capped at around GPT4o levels.

DeepSeek-R1 builds on the foundation ... and overall performance. DeepSeek-R1-Zero: Trained entirely with reinforcement learning (RL) and no supervised fine-tuning (SFT)....

DeepSeek-R1-Zero is a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step. It's 671B parameters in size, with 37B active in an inference pass. Run DeepSeek R1 Zero with API

DeepSeek's latest R-1 model is the most powerful open-source reasoning model that performs on par with OpenAI's o1 model. Learn how to run & fine-tune the model.

DeepSeek-R1-Zero demonstrates capabilities such as self-verification, reflection, and generating long CoTs, marking a significant milestone for the research community. Notably, it is the first open research to validate that reasoning capabilities ...

DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrated remarkable performance on reasoning.

January 20, 2025 - DeepSeek-R1 achieves performance comparable to OpenAI’s latest o1 model on reasoning tasks, including a 79.8 percent pass rate on AIME 2024 and 97.3 percent on MATH-500. The model, along with the reinforcement-learning-trained R1-Zero and smaller distilled versions, is now available under an MIT license, allowing open access for the community to use the model weights and outputs.

January 31, 2025 - The RL loop that produces R1-Zero is the core of the reasoning training, but it’s not the only step before the final R1 model is trained. Building on this checkpoint, DeepSeek curates a cold-start dataset (partly including cleaned up R1-Zero outputs) to fine-tune the base v3 model before ...

September 17, 2025 - This design choice originates from our hypothesis that human-defined reasoning patterns may limit model exploration, whereas unrestricted RL training can better incentivize the emergence of new reasoning capabilities in LLMs. Through this process, detailed in the next section, our model (referred to as DeepSeek-R1-Zero) naturally developed diverse and sophisticated reasoning behaviours.