DeepSeek-R1 is an open-source language model built on DeepSeek-V3-Base that's been making waves in the AI community. Not only does it match-or even surpass-OpenAI's o1 model in numerous benchmarks, however it likewise includes fully MIT-licensed weights. This marks it as the first non-OpenAI/Google model to deliver strong reasoning abilities in an open and available way.
What makes DeepSeek-R1 particularly amazing is its openness. Unlike the less-open methods from some industry leaders, DeepSeek has released a detailed training method in their paper.
The model is also extremely economical, 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 wisdom was that much better designs needed more information and calculate. While that's still valid, designs like o1 and R1 show an option: inference-time scaling through reasoning.
The Essentials
The DeepSeek-R1 paper provided numerous designs, but main amongst them were R1 and R1-Zero. Following these are a series of distilled designs that, while intriguing, I won't talk about 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 knowing method that counts on comparing multiple model outputs per prompt to avoid the need for a different critic.
R1 and R1-Zero are both reasoning designs. This basically suggests they do Chain-of-Thought before answering. For the R1 series of designs, this takes kind as believing within a tag, before responding to with a final summary.
R1-Zero vs R1
R1-Zero uses Reinforcement Learning (RL) straight to DeepSeek-V3-Base with no supervised fine-tuning (SFT). RL is used to enhance the design's policy to maximize reward.
R1-Zero attains outstanding precision but in some cases produces complicated outputs, such as mixing several languages in a single action. R1 repairs that by including restricted supervised fine-tuning and multiple RL passes, which enhances both accuracy and readability.
It is fascinating how some languages may express certain concepts much better, which leads the design to choose the most meaningful language for the job.
Training Pipeline
The training pipeline that DeepSeek published in the R1 paper is profoundly fascinating. It showcases how they produced such strong reasoning models, ura.cc and what you can expect from each stage. This includes the problems that the resulting from each stage have, and how they resolved it in the next phase.
It's fascinating that their training pipeline varies from the typical:
The normal training method: Pretraining on large dataset (train to predict next word) to get the base model → monitored fine-tuning → preference tuning via 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 couple of thousand Chain-of-Thought (CoT) samples to guarantee the RL procedure has a good starting point. This gives a great model to begin RL.
First RL Stage: Apply GRPO with rule-based rewards to improve reasoning correctness and formatting (such as forcing chain-of-thought into believing tags). When they were near convergence in the RL process, they relocated to the next action. The outcome of this action is a strong reasoning model but with weak general abilities, e.g., poor formatting and language blending.
Rejection Sampling + general data: Create brand-new SFT data through rejection tasting on the RL checkpoint (from step 2), integrated with monitored data from the DeepSeek-V3-Base model. 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 jobs) for broader abilities. This step led to a strong thinking model with general abilities.
Second RL Stage: Add more benefit signals (helpfulness, harmlessness) to fine-tune the last model, in addition to the thinking rewards. The result is DeepSeek-R1.
They also did model distillation for numerous Qwen and Llama models on the thinking traces to get distilled-R1 models.
Model distillation is a strategy where you use a teacher design to enhance a trainee model by generating training information for the trainee design.
The teacher is usually a larger model than the trainee.
Group Relative Policy Optimization (GRPO)
The standard concept behind utilizing support knowing for LLMs is to fine-tune the model's policy so that it naturally produces more accurate and beneficial responses.
They used a benefit system that checks not only for correctness however likewise for appropriate format and language consistency, so the model gradually discovers to prefer responses that meet these quality criteria.
In this paper, they motivate the R1 model to generate chain-of-thought thinking 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 habits of the optimized policy.
What makes their approach especially intriguing is its reliance on straightforward, rule-based benefit functions.
Instead of depending upon expensive external models or human-graded examples as in standard RLHF, the RL utilized for R1 utilizes simple requirements: it might offer a greater benefit if the response is right, if it follows the expected/ format, and if the language of the answer matches that of the timely.
Not counting on a benefit design likewise suggests you do not have to invest time and effort training it, and it doesn't take memory and compute far from your main model.
GRPO was presented in the DeepSeekMath paper. Here's how GRPO works:
1. For each input prompt, the design produces various responses.
2. Each reaction receives a scalar reward based upon factors like precision, format, and language consistency.
3. Rewards are changed relative to the group's efficiency, essentially determining just how much better each reaction is compared to the others.
4. The model updates its technique somewhat to prefer actions with higher relative advantages. It only makes minor adjustments-using strategies like clipping and a KL penalty-to make sure the policy does not wander off too far from its initial habits.
A cool aspect of GRPO is its versatility. You can utilize simple rule-based benefit functions-for circumstances, granting a benefit when the model correctly utilizes the syntax-to guide the training.
While DeepSeek used GRPO, you could utilize alternative techniques instead (PPO or PRIME).
For those aiming to dive deeper, Will Brown has written rather a nice execution of training an LLM with RL utilizing GRPO. GRPO has actually also currently 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 path to AGI?
As a last note on explaining DeepSeek-R1 and the methodologies they've presented 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 indicate that RL improves the model's general performance by rendering the output distribution more robust, to put it simply, it appears that the enhancement is credited to improving the appropriate action from TopK instead of the enhancement of essential abilities.
In other words, RL fine-tuning tends to shape the output distribution so that the highest-probability outputs are most likely to be correct, even though the overall capability (as determined by the variety of appropriate answers) is mainly present in the pretrained model.
This recommends that support knowing on LLMs is more about refining and "forming" the existing circulation of reactions instead of enhancing the design with completely new capabilities.
Consequently, while RL strategies such as PPO and GRPO can produce considerable efficiency gains, there seems a fundamental ceiling identified by the underlying design's pretrained understanding.
It is uncertain to me how far RL will take us. Perhaps it will be the stepping stone to the next big turning point. I'm excited to see how it unfolds!
Running DeepSeek-R1
I've utilized DeepSeek-R1 via the main chat interface for different problems, which it seems to resolve all right. The additional search functionality makes it even nicer to use.
Interestingly, o3-mini(-high) was launched as I was composing this post. From my initial testing, R1 seems stronger 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 objective was to see how the model would perform when deployed on a single H100 GPU-not to extensively evaluate the design's capabilities.
671B through 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 area provided this setup.
Performance:
A r/localllama user explained that they were able to get over 2 tok/sec with DeepSeek R1 671B, without utilizing their GPU on their local video gaming setup.
Digital Spaceport wrote a complete guide on how to run Deepseek R1 671b fully 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 serious work, however 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 reasoning models require to believe before addressing, their time-to-usefulness is generally higher than other models, but their usefulness is also usually greater.
We need to both make the most of usefulness and lessen time-to-usefulness.
70B through Ollama
70.6 b params, 4-bit KM quantized DeepSeek-R1 running through Ollama:
GPU utilization shoots up here, as expected when compared to the mainly CPU-powered run of 671B that I showcased above.
Resources
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
[2402.03300] DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
DeepSeek R1 - Notion (Building a totally local "deep scientist" with DeepSeek-R1 - YouTube).
DeepSeek R1's recipe 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): Janus-Pro is an unique autoregressive framework that unifies multimodal understanding and generation. It can both understand and create images.
DeepSeek-R1: Incentivizing Reasoning Capability in Large Language Models through Reinforcement Learning (January 2025) This paper introduces DeepSeek-R1, an open-source thinking model that equals the performance of OpenAI's o1. It provides a detailed approach for training such models using massive reinforcement learning methods.
DeepSeek-V3 Technical Report (December 2024) This report talks about the execution of an FP8 blended accuracy training framework validated on an exceptionally massive design, attaining both accelerated training and lowered GPU memory usage.
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism (January 2024) This paper digs into scaling laws and presents findings that facilitate the scaling of large-scale models in open-source configurations. It introduces the DeepSeek LLM project, devoted to advancing open-source language designs with a long-term 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 models trained from scratch on 2 trillion tokens. The designs are pre-trained on a premium project-level code corpus and utilize 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 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 design that attains performance equivalent to GPT-4 Turbo in code-specific jobs.
Interesting events
- Hong Kong University reproduces R1 results (Jan 25, '25).
- Huggingface announces huggingface/open-r 1: Fully open recreation of DeepSeek-R1 to duplicate R1, totally open source (Jan 25, '25).
- OpenAI researcher confirms the DeepSeek team separately found and utilized some core ideas the OpenAI team used en route to o1
Liked this post? Join the newsletter.