Understanding DeepSeek R1

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DeepSeek-R1 is an open-source language model built on DeepSeek-V3-Base that's been making waves in the AI neighborhood. Not just does it match-or even surpass-OpenAI's o1 design in numerous standards, but it also comes with fully MIT-licensed weights. This marks it as the very first non-OpenAI/Google design to deliver strong reasoning capabilities in an open and available way.


What makes DeepSeek-R1 especially interesting is its transparency. Unlike the less-open methods from some industry leaders, DeepSeek has released a detailed training methodology in their paper.
The model is likewise incredibly 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 common wisdom was that better designs needed more data and calculate. While that's still valid, designs like o1 and R1 demonstrate an option: inference-time scaling through reasoning.


The Essentials


The DeepSeek-R1 paper presented numerous designs, but main among them were R1 and R1-Zero. Following these are a series of distilled models that, while fascinating, I won't discuss here.


DeepSeek-R1 utilizes two significant concepts:


1. A multi-stage pipeline where a small set of cold-start data kickstarts the model, followed by large-scale RL.
2. Group Relative Policy Optimization (GRPO), a support learning method that depends on comparing several model outputs per timely to avoid the need for a separate critic.


R1 and R1-Zero are both reasoning designs. This essentially implies they do Chain-of-Thought before answering. For the R1 series of designs, this takes kind as thinking within a tag, before addressing with a final summary.


R1-Zero vs R1


R1-Zero uses Reinforcement Learning (RL) straight to DeepSeek-V3-Base without any monitored fine-tuning (SFT). RL is used to enhance the model's policy to maximize reward.
R1-Zero attains outstanding accuracy but sometimes produces confusing outputs, such as blending several languages in a single reaction. R1 repairs that by including restricted monitored fine-tuning and several RL passes, which improves both correctness and readability.


It is intriguing how some languages might reveal certain concepts better, which leads the design to select the most expressive language for the task.


Training Pipeline


The training pipeline that DeepSeek published in the R1 paper is immensely intriguing. It showcases how they developed such strong thinking models, and what you can expect from each stage. This includes the problems that the resulting designs from each phase have, and how they resolved it in the next phase.


It's intriguing that their training pipeline varies from the usual:


The typical training method: Pretraining on big dataset (train to anticipate next word) to get the base design → monitored fine-tuningchoice tuning by means of RLHF
R1-Zero: Pretrained → RL
R1: Pretrained → Multistage training pipeline with several 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 process has a decent beginning point. This gives a great model to begin RL.
First RL Stage: Apply GRPO with rule-based benefits to enhance thinking accuracy and format (such as requiring chain-of-thought into thinking tags). When they were near convergence in the RL process, they moved to the next action. The result of this step is a strong thinking model but with weak general capabilities, e.g., bad format and language blending.
Rejection Sampling + general data: Create brand-new SFT data through rejection tasting on the RL checkpoint (from action 2), combined with supervised information from the DeepSeek-V3-Base design. They collected around 600k high-quality thinking samples.
Second Fine-Tuning: Fine-tune DeepSeek-V3-Base again on 800k total samples (600k thinking + 200k general tasks) for broader abilities. This action resulted in a strong reasoning model with basic abilities.
Second RL Stage: Add more benefit signals (helpfulness, harmlessness) to fine-tune the last model, in addition to the thinking benefits. The result is DeepSeek-R1.
They likewise did model distillation for numerous Qwen and Llama models on the reasoning traces to get distilled-R1 designs.


Model distillation is a technique where you utilize an instructor design to enhance a trainee design by producing training data for the trainee design.
The teacher is normally a larger design than the trainee.


Group Relative Policy Optimization (GRPO)


The fundamental concept behind utilizing reinforcement knowing for LLMs is to fine-tune the design's policy so that it naturally produces more precise and beneficial answers.
They used a benefit system that examines not only for accuracy but likewise for appropriate format and language consistency, so the design gradually learns to favor actions that meet these .


In this paper, they motivate the R1 design to produce chain-of-thought reasoning through RL training with GRPO.
Instead of adding a separate module at reasoning time, the training procedure itself nudges the model to produce detailed, detailed outputs-making the chain-of-thought an emergent behavior of the optimized policy.


What makes their method especially intriguing is its reliance on straightforward, rule-based reward functions.
Instead of depending upon pricey external designs or human-graded examples as in conventional RLHF, the RL used for R1 uses simple criteria: it may give a greater benefit if the response is correct, if it follows the expected/ formatting, and if the language of the response matches that of the timely.
Not counting on a benefit model likewise suggests you don't need to invest time and effort training it, and it doesn't take memory and calculate away from your main model.


GRPO was presented in the DeepSeekMath paper. Here's how GRPO works:


1. For each input timely, the design generates various actions.
2. Each action receives a scalar benefit based on elements like precision, format, and language consistency.
3. Rewards are adjusted relative to the group's efficiency, basically measuring how much better each action is compared to the others.
4. The design updates its method somewhat to prefer actions with higher relative benefits. It just makes slight adjustments-using strategies like clipping and a KL penalty-to ensure the policy does not wander off too far from its original behavior.


A cool element of GRPO is its flexibility. You can use simple rule-based reward functions-for circumstances, granting a perk when the model properly uses the syntax-to guide the training.


While DeepSeek utilized GRPO, you could use alternative techniques rather (PPO or PRIME).


For those aiming to dive deeper, Will Brown has composed rather a nice application of training an LLM with RL utilizing GRPO. GRPO has actually also already been included to the Transformer Reinforcement Learning (TRL) library, which is another good resource.
Finally, Yannic Kilcher has an excellent video explaining GRPO by going through the DeepSeekMath paper.


Is RL on LLMs the course to AGI?


As a final note on explaining DeepSeek-R1 and the methods they have actually provided in their paper, I desire to highlight a passage from the DeepSeekMath paper, based upon a point Yannic Kilcher made in his video.


These findings suggest that RL improves the model's overall efficiency by rendering the output circulation more robust, to put it simply, it appears that the improvement is credited to increasing the correct response from TopK instead of the enhancement of basic capabilities.


Simply put, RL fine-tuning tends to form the output distribution so that the highest-probability outputs are most likely to be correct, although the total ability (as measured by the diversity of right responses) is mainly present in the pretrained design.


This suggests that reinforcement learning on LLMs is more about refining and "shaping" the existing circulation of actions instead of endowing the model with completely brand-new abilities.
Consequently, while RL methods such as PPO and GRPO can produce significant performance gains, there appears to be an intrinsic ceiling identified by the underlying model's pretrained knowledge.


It is uncertain to me how far RL will take us. Perhaps it will be the stepping stone to the next huge milestone. I'm excited to see how it unfolds!


Running DeepSeek-R1


I've used DeepSeek-R1 through the main chat interface for various problems, which it appears to solve all right. The extra search functionality makes it even better to utilize.


Interestingly, o3-mini(-high) was launched as I was writing this post. From my preliminary testing, R1 appears more powerful at mathematics than o3-mini.


I also rented a single H100 by means of Lambda Labs for $2/h (26 CPU cores, 214.7 GB RAM, 1.1 TB SSD) to run some experiments.
The main goal was to see how the model would perform when released on a single H100 GPU-not to thoroughly check the model's abilities.


671B via Llama.cpp


DeepSeek-R1 1.58-bit (UD-IQ1_S) quantized design by Unsloth, with a 4-bit quantized KV-cache and partial GPU offloading (29 layers operating on the GPU), running via llama.cpp:


29 layers seemed 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 utilizing their GPU on their local gaming setup.
Digital Spaceport composed a full guide on how to run Deepseek R1 671b completely in your area 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 bearable for any severe work, but it's enjoyable to run these large models on available hardware.


What matters most to me is a combination of effectiveness and time-to-usefulness in these designs. Since thinking models need to think before responding to, their time-to-usefulness is usually greater than other models, but their usefulness is likewise generally greater.
We require to both make the most of usefulness and reduce time-to-usefulness.


70B through Ollama


70.6 b params, 4-bit KM quantized DeepSeek-R1 running by means of 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 through Reinforcement Learning
[2402.03300] DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
DeepSeek R1 - Notion (Building a fully regional "deep scientist" with DeepSeek-R1 - YouTube).
DeepSeek R1's dish to duplicate 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): drapia.org Janus-Pro is a novel autoregressive structure that combines multimodal understanding and generation. It can both comprehend and produce images.
DeepSeek-R1: Incentivizing Reasoning Capability in Large Language Models by means of Reinforcement Learning (January 2025) This paper presents DeepSeek-R1, addsub.wiki an open-source reasoning model that matches the efficiency of OpenAI's o1. It presents a detailed approach for training such models using large-scale support knowing techniques.
DeepSeek-V3 Technical Report (December 2024) This report discusses the application of an FP8 blended accuracy training framework validated on an incredibly massive model, attaining both sped up training and lowered GPU memory use.
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism (January 2024) This paper looks into scaling laws and provides findings that assist in the scaling of massive designs in open-source setups. It introduces the DeepSeek LLM project, devoted to advancing open-source language designs with a long-lasting point of view.
DeepSeek-Coder: When the Large Language Model Meets Programming-The Rise of Code Intelligence (January 2024) This research presents the DeepSeek-Coder series, a series of open-source code designs trained from scratch on 2 trillion tokens. The designs are pre-trained on a top quality project-level code corpus and use a fill-in-the-blank job to enhance 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 economical 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 performance comparable to GPT-4 Turbo in code-specific jobs.


Interesting events


- Hong Kong University replicates R1 results (Jan 25, '25).
- Huggingface reveals huggingface/open-r 1: Fully open recreation of DeepSeek-R1 to duplicate R1, completely open source (Jan 25, '25).
- OpenAI scientist confirms the DeepSeek group independently discovered and utilized some core ideas the OpenAI group utilized en route to o1


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