DeepSeek-R1 is an open-source language design developed on DeepSeek-V3-Base that's been making waves in the AI community. Not just does it match-or even surpass-OpenAI's o1 design in lots of criteria, but it likewise comes with totally MIT-licensed weights. This marks it as the very first non-OpenAI/Google model to deliver strong thinking abilities in an open and available manner.

What makes DeepSeek-R1 especially interesting is its openness. Unlike the less-open methods from some market leaders, DeepSeek has published a detailed training approach in their paper. The model is also extremely affordable, with input tokens costing just $0.14-0.55 per million (vs o1's $15) and output tokens at $2.19 per million (vs o1's $60).

Until ~ GPT-4, the typical knowledge was that much better models needed more data and compute. While that's still legitimate, designs like o1 and R1 show an alternative: inference-time scaling through thinking.

The Essentials

The DeepSeek-R1 paper presented multiple designs, however main among them were R1 and R1-Zero. Following these are a series of distilled designs that, while interesting, I will not discuss here.

DeepSeek-R1 utilizes 2 major concepts:

1. A multi-stage pipeline where a small set of cold-start data kickstarts the design, followed by massive RL.

1. Group Relative Policy Optimization (GRPO), a reinforcement knowing approach that depends on comparing numerous design outputs per timely to prevent the need for a different critic.
   
   R1 and R1-Zero are both thinking designs. This essentially implies they do Chain-of-Thought before addressing. For the R1 series of designs, this takes kind as believing within a tag, before addressing with a last summary.
   
   R1-Zero vs R1
   
   R1-Zero applies Reinforcement Learning (RL) straight to DeepSeek-V3-Base without any supervised fine-tuning (SFT). RL is utilized to enhance the model's policy to take full advantage of benefit. R1-Zero attains excellent precision however sometimes produces complicated outputs, such as blending multiple languages in a single response. R1 repairs that by incorporating minimal supervised fine-tuning and several RL passes, which enhances both correctness and readability.
   
   It is interesting how some languages may express certain concepts better, which leads the design to pick the most expressive language for the job.
   
   Training Pipeline
   
   The training pipeline that DeepSeek published in the R1 paper is exceptionally interesting. It showcases how they developed such strong thinking designs, and what you can anticipate from each stage. This consists of the problems that the resulting designs from each phase have, and how they resolved it in the next phase.
   
   It's fascinating that their training pipeline differs from the normal:
   
   The usual training strategy: Pretraining on big dataset (train to predict next word) to get the base model → monitored fine-tuning → choice tuning through RLHF R1-Zero: Pretrained → RL R1: Pretrained → Multistage training pipeline with multiple SFT and RL stages
   
   Cold-Start Fine-Tuning: Fine-tune DeepSeek-V3-Base on a few thousand Chain-of-Thought (CoT) samples to make sure the RL procedure has a decent beginning point. This provides a good model to begin RL. First RL Stage: Apply GRPO with rule-based rewards to improve reasoning accuracy and format (such as forcing chain-of-thought into believing tags). When they were near merging in the RL process, they relocated to the next step. The result of this action is a strong reasoning model but with weak general capabilities, e.g., bad format and language blending. Rejection Sampling + general data: Create new SFT information through rejection sampling on the RL checkpoint (from step 2), integrated with supervised data from the DeepSeek-V3-Base design. They gathered around 600k high-quality thinking samples. Second Fine-Tuning: Fine-tune DeepSeek-V3-Base again on 800k overall samples (600k thinking + 200k general tasks) for broader abilities. This action led to a strong thinking design with general capabilities. Second RL Stage: Add more reward signals (helpfulness, harmlessness) to improve the final model, funsilo.date in addition to the reasoning rewards. The outcome is DeepSeek-R1. They also did design distillation for a number of Qwen and Llama models on the thinking traces to get distilled-R1 models.
   
   Model distillation is a method where you use a teacher design to improve a trainee model by creating training information for the trainee model. The teacher is usually a bigger model than the trainee.
   
   Group Relative Policy Optimization (GRPO)
   
   The basic concept behind using support learning for LLMs is to fine-tune the model's policy so that it naturally produces more accurate and beneficial responses. They used a reward system that checks not just for accuracy but also for correct formatting and language consistency, so the design slowly discovers to prefer actions that satisfy these quality criteria.
   
   In this paper, they encourage the R1 model to produce chain-of-thought reasoning through RL training with GRPO. Rather than including a different module at reasoning time, the training process itself nudges the model to produce detailed, detailed outputs-making the chain-of-thought an emerging habits of the enhanced policy.
   
   What makes their approach particularly intriguing is its dependence on straightforward, rule-based reward functions. Instead of depending upon costly external models or human-graded examples as in standard RLHF, the RL utilized for R1 uses simple criteria: it might offer a higher benefit if the answer is appropriate, if it follows the expected/ format, and if the language of the answer matches that of the prompt. Not relying on a benefit model also implies you don't need to hang around and effort training it, and it doesn't take memory and calculate far from your main model.
   
   GRPO was introduced in the DeepSeekMath paper. Here's how GRPO works:
   
   1. For each input prompt, the model creates various reactions.
2. Each action gets a scalar reward based on factors like accuracy, format, and language consistency.
3. Rewards are changed relative to the group's performance, essentially determining just how much better each action is compared to the others.
4. The model updates its strategy slightly to prefer responses with higher relative benefits. It just makes minor adjustments-using methods like clipping and a KL penalty-to ensure the policy does not stray too far from its initial behavior.
   
   A cool aspect of GRPO is its flexibility. You can use basic rule-based reward functions-for instance, awarding a perk when the design properly uses the syntax-to guide the training.
   
   While DeepSeek utilized GRPO, you might utilize alternative techniques rather (PPO or PRIME).
   
   For those aiming to dive deeper, Will Brown has composed quite a great execution of training an LLM with RL using GRPO. GRPO has likewise already been contributed to the Transformer Reinforcement Learning (TRL) library, which is another great resource. Finally, Yannic Kilcher has a fantastic video explaining GRPO by going through the DeepSeekMath paper.
   
   Is RL on LLMs the course to AGI?
   
   As a last note on explaining DeepSeek-R1 and the methods they have actually provided in their paper, I wish to highlight a passage from the DeepSeekMath paper, based on a point Yannic Kilcher made in his video.
   
   These findings show that RL enhances the model's overall performance by rendering the output distribution more robust, in other words, it seems that the improvement is associated to enhancing the right response from TopK rather than the enhancement of fundamental capabilities.
   
   To put it simply, RL fine-tuning tends to shape the output distribution so that the highest-probability outputs are most likely to be right, although the total capability (as determined by the diversity of appropriate answers) is mainly present in the pretrained design.
   
   This recommends that support knowing on LLMs is more about refining and "forming" the existing distribution of reactions rather than enhancing the model with entirely brand-new capabilities. Consequently, while RL methods such as PPO and GRPO can produce significant performance gains, there appears to be an inherent ceiling determined by the underlying design's pretrained knowledge.
   
   It is uncertain to me how far RL will take us. Perhaps it will be the stepping stone to the next big milestone. I'm thrilled to see how it unfolds!
   
   Running DeepSeek-R1
   
   I have actually utilized DeepSeek-R1 by means of the main chat user interface for various issues, which it seems to solve all right. The additional search performance makes it even better to use.
   
   Interestingly, o3-mini(-high) was launched as I was composing this post. From my initial screening, R1 seems more powerful at math than o3-mini.
   
   I also leased a single H100 through Lambda Labs for $2/h (26 CPU cores, 214.7 GB RAM, 1.1 TB SSD) to run some experiments. The main objective was to see how the model would perform when on a single H100 GPU-not to extensively evaluate the design's capabilities.
   
   671B by means of Llama.cpp
   
   DeepSeek-R1 1.58-bit (UD-IQ1_S) quantized model by Unsloth, with a 4-bit quantized KV-cache and partial GPU offloading (29 layers operating on the GPU), running by means of llama.cpp:
   
   29 layers appeared to be the sweet spot offered this configuration.
   
   Performance:
   
   A r/localllama user explained that they were able to overcome 2 tok/sec with DeepSeek R1 671B, without using their GPU on their local video gaming setup. Digital Spaceport composed a complete guide on how to run Deepseek R1 671b totally locally on a $2000 EPYC server, on which you can get ~ 4.25 to 3.5 tokens per second.
   
   As you can see, the tokens/s isn't quite manageable for any major work, but it's enjoyable to run these large models on available hardware.
   
   What matters most to me is a mix of usefulness and time-to-usefulness in these designs. Since thinking designs require to believe before addressing, their time-to-usefulness is normally greater than other models, but their usefulness is also typically greater. We require to both make the most of usefulness and minimize time-to-usefulness.
   
   70B via Ollama
   
   70.6 b params, 4-bit KM quantized DeepSeek-R1 running via Ollama:
   
   GPU utilization soars here, as anticipated when compared to the mainly CPU-powered run of 671B that I showcased above.
   
   Resources
   
   DeepSeek-R1: Incentivizing Reasoning Capability in LLMs by means of Reinforcement Learning [2402.03300] DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models DeepSeek R1 - Notion (Building a completely regional "deep researcher" with DeepSeek-R1 - YouTube). DeepSeek R1's recipe to reproduce o1 and the future of thinking LMs. The Illustrated DeepSeek-R1 - by Jay Alammar. Explainer: What's R1 & Everything Else? - Tim Kellogg. DeepSeek R1 Explained to your grandmother - YouTube
   
   DeepSeek
   
   - Try R1 at chat.deepseek.com. GitHub - deepseek-ai/DeepSeek-R 1. deepseek-ai/Janus-Pro -7 B · Hugging Face (January 2025): Janus-Pro is a novel autoregressive framework that combines multimodal understanding and generation. It can both comprehend and generate images. DeepSeek-R1: Incentivizing Reasoning Capability in Large Language Models by means of Reinforcement Learning (January 2025) This paper introduces DeepSeek-R1, an open-source thinking model that rivals the efficiency of OpenAI's o1. It presents a detailed methodology for training such designs utilizing large-scale reinforcement learning techniques. DeepSeek-V3 Technical Report (December 2024) This report talks about the execution of an FP8 mixed accuracy training framework verified on an exceptionally massive model, attaining both accelerated training and lowered GPU memory usage. DeepSeek LLM: Scaling Open-Source Language Models with Longtermism (January 2024) This paper looks into scaling laws and presents findings that help with the scaling of massive models in open-source configurations. It presents the DeepSeek LLM project, dedicated to advancing open-source language designs with a long-term perspective. DeepSeek-Coder: When the Large Language Model Meets Programming-The Rise of Code Intelligence (January 2024) This research study introduces the DeepSeek-Coder series, a series of open-source code designs trained from scratch on 2 trillion tokens. The models are pre-trained on a top quality project-level code corpus and employ a fill-in-the-blank task to boost code generation and infilling. DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model (May 2024) This paper provides DeepSeek-V2, a Mixture-of-Experts (MoE) language design identified by affordable training and efficient reasoning. DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence (June 2024) This research study introduces DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that attains efficiency equivalent to GPT-4 Turbo in code-specific jobs.
   
   Interesting events
   
   - Hong Kong University duplicates R1 results (Jan 25, '25). - Huggingface announces huggingface/open-r 1: Fully open reproduction of DeepSeek-R1 to reproduce R1, totally open source (Jan 25, '25).
5. OpenAI scientist verifies the DeepSeek team separately discovered and utilized some core ideas the OpenAI group used en route to o1
   
   Liked this post? Join the newsletter.