DeepSeek-R1 is an open-source language model developed on DeepSeek-V3-Base that's been making waves in the AI community. Not only does it match-or even surpass-OpenAI's o1 design in numerous standards, but it likewise includes fully MIT-licensed weights. This marks it as the very first non-OpenAI/Google design to deliver strong reasoning abilities in an open and available manner.
What makes DeepSeek-R1 particularly amazing is its transparency. Unlike the less-open methods from some industry leaders, DeepSeek has released a detailed training approach in their paper.
The design is likewise extremely cost-efficient, with input tokens costing simply $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 information and compute. While that's still legitimate, designs like o1 and R1 demonstrate an alternative: inference-time scaling through thinking.
The Essentials
The DeepSeek-R1 paper provided multiple models, but main among them were R1 and R1-Zero. Following these are a series of distilled designs that, while interesting, I won't go over here.
DeepSeek-R1 utilizes 2 significant concepts:
1. A multi-stage pipeline where a little set of cold-start data kickstarts the model, followed by massive RL.
2. Group Relative Policy Optimization (GRPO), a reinforcement learning approach that counts on comparing several model outputs per prompt to avoid the need for a separate critic.
R1 and R1-Zero are both thinking designs. This essentially indicates they do Chain-of-Thought before responding to. For the R1 series of models, this takes type as thinking 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 monitored fine-tuning (SFT). RL is utilized to enhance the design's policy to maximize reward.
R1-Zero attains excellent accuracy but in some cases produces confusing outputs, such as mixing several languages in a single reaction. R1 repairs that by integrating minimal monitored fine-tuning and several RL passes, which improves both correctness and readability.
It is intriguing how some languages may express certain ideas better, which leads the model to select the most meaningful language for the task.
Training Pipeline
The training pipeline that DeepSeek published in the R1 paper is exceptionally intriguing. It showcases how they created such strong thinking designs, and what you can anticipate from each stage. This consists of the problems that the resulting models from each stage have, and how they solved it in the next stage.
It's fascinating that their training pipeline differs from the usual:
The normal training method: Pretraining on large dataset (train to forecast next word) to get the base design → monitored fine-tuning → preference tuning through RLHF
R1-Zero: Pretrained → RL
R1: Pretrained → Multistage training pipeline with multiple SFT and RL phases
Cold-Start Fine-Tuning: Fine-tune DeepSeek-V3-Base on a few thousand Chain-of-Thought (CoT) samples to guarantee the RL process has a good starting point. This offers an excellent design to start RL.
First RL Stage: Apply GRPO with rule-based rewards to improve reasoning accuracy and formatting (such as forcing chain-of-thought into thinking tags). When they were near convergence in the RL procedure, they transferred to the next action. The result of this action is a strong thinking design but with weak general capabilities, e.g., poor format and language blending.
Rejection Sampling + basic information: Create brand-new SFT data through rejection sampling on the RL checkpoint (from action 2), combined with monitored information from the DeepSeek-V3-Base design. They collected around 600k top quality reasoning samples.
Second Fine-Tuning: Fine-tune DeepSeek-V3-Base again on 800k total samples (600k thinking + 200k basic tasks) for tandme.co.uk more comprehensive abilities. This action led to a strong reasoning model with general abilities.
Second RL Stage: Add more benefit signals (helpfulness, bio.rogstecnologia.com.br harmlessness) to refine the last model, in addition to the reasoning benefits. The result is DeepSeek-R1.
They also did model distillation for several Qwen and Llama models on the thinking traces to get distilled-R1 designs.
Model distillation is a strategy where you use an instructor model to improve a trainee model by creating training data for the trainee design.
The instructor is normally a larger model than the trainee.
Group Relative Policy Optimization (GRPO)
The basic idea behind utilizing support knowing for LLMs is to tweak the design's policy so that it naturally produces more accurate and helpful responses.
They utilized a benefit system that examines not just for correctness however likewise for correct formatting and language consistency, so the design slowly discovers to favor responses that meet these quality requirements.
In this paper, they motivate the R1 design to produce chain-of-thought thinking through RL training with GRPO.
Rather than including a different module at inference time, the training procedure itself pushes the model to produce detailed, detailed outputs-making the chain-of-thought an emerging behavior of the optimized policy.
What makes their approach particularly interesting is its dependence on straightforward, rule-based reward functions.
Instead of depending upon expensive external designs or human-graded examples as in conventional RLHF, the RL used for R1 utilizes easy criteria: it may offer a higher benefit if the response is correct, if it follows the expected/ format, and if the language of the that of the prompt.
Not depending on a benefit design likewise indicates you don't need to hang around and effort training it, and it doesn't take memory and calculate away from your main design.
GRPO was introduced in the DeepSeekMath paper. Here's how GRPO works:
1. For each input prompt, the model produces different responses.
2. Each response gets a scalar benefit based on factors like accuracy, format, and language consistency.
3. Rewards are changed relative to the group's efficiency, essentially measuring how much better each reaction is compared to the others.
4. The model updates its strategy a little to prefer responses with greater relative advantages. It only makes slight adjustments-using techniques like clipping and a KL penalty-to guarantee the policy does not stray too far from its initial habits.
A cool aspect of GRPO is its versatility. You can utilize easy rule-based reward functions-for instance, awarding a benefit when the design properly uses the syntax-to guide the training.
While DeepSeek utilized GRPO, you might utilize alternative methods rather (PPO or PRIME).
For those aiming to dive deeper, Will Brown has composed quite a great implementation of training an LLM with RL using GRPO. GRPO has actually also already been added to the Transformer Reinforcement Learning (TRL) library, which is another excellent resource.
Finally, Yannic Kilcher has a terrific video explaining GRPO by going through the DeepSeekMath paper.
Is RL on LLMs the path to AGI?
As a final note on explaining DeepSeek-R1 and the methodologies they've presented in their paper, I wish to highlight a passage from the DeepSeekMath paper, based upon a point Yannic Kilcher made in his video.
These findings suggest that RL enhances the model's general performance by rendering the output distribution more robust, simply put, dokuwiki.stream it appears that the enhancement is associated to improving the correct response from TopK rather than the improvement of fundamental capabilities.
To put it simply, RL fine-tuning tends to shape the output circulation so that the highest-probability outputs are most likely to be correct, despite the fact that the general capability (as determined by the diversity of proper answers) is mainly present in the pretrained design.
This suggests that support knowing on LLMs is more about refining and "shaping" the existing distribution of responses rather than enhancing the design with entirely new abilities.
Consequently, while RL methods such as PPO and GRPO can produce significant efficiency gains, there seems an inherent ceiling determined 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 thrilled to see how it unfolds!
Running DeepSeek-R1
I've utilized DeepSeek-R1 by means of the main chat user interface for various issues, which it seems to resolve well enough. The additional search functionality makes it even better to utilize.
Interestingly, o3-mini(-high) was released as I was composing this post. From my initial testing, R1 seems more powerful at mathematics than o3-mini.
I likewise rented 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 goal was to see how the model would carry out when deployed 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 appeared to be the sweet area provided this setup.
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 full guide on how to run Deepseek R1 671b completely 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 fun 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 require to think before addressing, their time-to-usefulness is usually greater than other designs, however their usefulness is also typically greater.
We need to both make the most of usefulness and decrease time-to-usefulness.
70B via Ollama
70.6 b params, 4-bit KM quantized DeepSeek-R1 running via Ollama:
GPU utilization soars 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 through Reinforcement Learning
[2402.03300] DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
DeepSeek R1 - Notion (Building a completely local "deep scientist" 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 granny - 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 comprehend and create images.
DeepSeek-R1: Incentivizing Reasoning Capability in Large Language Models through Reinforcement Learning (January 2025) This paper presents DeepSeek-R1, an open-source thinking model that measures up to the performance of OpenAI's o1. It provides a detailed approach for training such models using massive support knowing strategies.
DeepSeek-V3 Technical Report (December 2024) This report goes over the execution of an FP8 combined accuracy training framework verified on an incredibly large-scale design, attaining both accelerated training and lowered GPU memory use.
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism (January 2024) This paper looks into scaling laws and presents findings that facilitate the scaling of large-scale designs in open-source setups. It introduces the DeepSeek LLM task, 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 study introduces the DeepSeek-Coder series, a variety of open-source code designs trained from scratch on 2 trillion tokens. The designs are pre-trained on a premium project-level code corpus and employ a fill-in-the-blank task to boost code generation and infilling.
DeepSeek-V2: utahsyardsale.com A Strong, Economical, and Efficient Mixture-of-Experts Language Model (May 2024) This paper presents DeepSeek-V2, a Mixture-of-Experts (MoE) language model identified by affordable training and efficient inference.
DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence (June 2024) This research introduces DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language design that attains performance similar to GPT-4 Turbo in code-specific tasks.
Interesting events
- Hong Kong University duplicates R1 outcomes (Jan 25, '25).
- Huggingface announces huggingface/open-r 1: Fully open recreation of DeepSeek-R1 to reproduce R1, completely open source (Jan 25, '25).
- OpenAI researcher validates the DeepSeek group separately found and utilized some core ideas the OpenAI team utilized en route to o1
Liked this post? Join the newsletter.