DeepSeek-R1 is an open-source language design built on DeepSeek-V3-Base that's been making waves in the AI neighborhood. Not only does it match-or even surpass-OpenAI's o1 design in many benchmarks, but it also includes completely MIT-licensed weights. This marks it as the first non-OpenAI/Google design to deliver strong reasoning abilities in an open and available manner.
What makes DeepSeek-R1 especially interesting is its transparency. Unlike the less-open techniques from some industry leaders, DeepSeek has released a detailed training method in their paper.
The design is also extremely cost-effective, 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 common knowledge was that much better models needed more data 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 multiple designs, but main among them were R1 and R1-Zero. Following these are a series of distilled designs that, while fascinating, I will not go over here.
DeepSeek-R1 utilizes two major concepts:
1. A multi-stage pipeline where a little set of cold-start information kickstarts the model, followed by massive RL.
2. Group Relative Policy Optimization (GRPO), a reinforcement knowing technique that depends on comparing numerous model outputs per prompt to avoid the requirement for a different critic.
R1 and R1-Zero are both thinking models. 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 addressing with a final summary.
R1-Zero vs R1
R1-Zero uses Reinforcement Learning (RL) straight to DeepSeek-V3-Base with no monitored fine-tuning (SFT). RL is utilized to optimize the model's policy to maximize reward.
R1-Zero attains outstanding precision however sometimes produces complicated outputs, such as blending multiple languages in a single action. R1 repairs that by incorporating minimal supervised fine-tuning and numerous RL passes, which improves both correctness and readability.
It is interesting how some languages might express certain ideas much better, which leads the design to choose the most expressive language for the task.
Training Pipeline
The training pipeline that DeepSeek released in the R1 paper is tremendously interesting. It showcases how they developed such strong reasoning designs, and what you can anticipate from each phase. This includes the problems that the resulting models from each phase have, and how they resolved it in the next stage.
It's fascinating that their training pipeline differs from the typical:
The normal training method: Pretraining on big dataset (train to forecast next word) to get the base design → monitored fine-tuning → preference tuning via RLHF
R1-Zero: Pretrained → RL
R1: Pretrained → Multistage training pipeline with numerous SFT and RL stages
Cold-Start Fine-Tuning: Fine-tune DeepSeek-V3-Base on a few thousand Chain-of-Thought (CoT) samples to ensure the RL procedure has a good starting point. This provides an excellent design to start RL.
First RL Stage: Apply GRPO with rule-based rewards to enhance reasoning accuracy and formatting (such as forcing chain-of-thought into believing tags). When they were near merging in the RL procedure, they transferred to the next step. The outcome of this action is a strong reasoning model however with weak general abilities, e.g., bad format and language blending.
Rejection Sampling + general data: Create brand-new SFT data through rejection sampling on the RL checkpoint (from action 2), combined with supervised data from the DeepSeek-V3-Base design. They collected around 600k premium reasoning samples.
Second Fine-Tuning: Fine-tune DeepSeek-V3-Base again on 800k overall samples (600k reasoning + 200k basic tasks) for broader capabilities. This action resulted in a strong thinking design with basic capabilities.
Second RL Stage: Add more reward signals (helpfulness, harmlessness) to refine the final design, in addition to the reasoning benefits. The result is DeepSeek-R1.
They likewise did model distillation for a number of Qwen and Llama designs on the reasoning traces to get distilled-R1 models.
Model distillation is a technique where you utilize an instructor model to enhance a trainee design by generating training information for the trainee design.
The teacher is generally a bigger design than the trainee.
Group Relative Policy Optimization (GRPO)
The fundamental idea behind using support knowing for LLMs is to fine-tune the model's policy so that it naturally produces more precise and useful answers.
They utilized a benefit system that checks not only for accuracy however likewise for correct format and language consistency, so the model gradually finds out to prefer responses that meet these quality requirements.
In this paper, wavedream.wiki they encourage the R1 model to generate chain-of-thought reasoning through RL training with GRPO.
Rather than including a separate module at inference time, the training process itself nudges the model to produce detailed, detailed outputs-making the chain-of-thought an emerging behavior of the optimized policy.
What makes their method particularly intriguing is its reliance on straightforward, rule-based benefit functions.
Instead of depending upon costly external models or human-graded examples as in traditional RLHF, the RL used for R1 uses simple requirements: demo.qkseo.in it may offer a higher benefit if the response is proper, if it follows the anticipated/ formatting, and if the language of the response matches that of the prompt.
Not depending on a reward design also indicates you do not have to hang out and effort training it, and it doesn't take memory and compute far from your main design.
GRPO was presented in the DeepSeekMath paper. Here's how GRPO works:
1. For each input timely, the model produces different actions.
2. Each action receives a scalar benefit based upon aspects like precision, format, and language consistency.
3. Rewards are adjusted relative to the group's performance, basically measuring how much better each action is compared to the others.
4. The design updates its strategy somewhat to prefer responses with greater relative advantages. It only makes minor adjustments-using strategies like clipping and a KL penalty-to make sure the policy doesn't stray too far from its initial behavior.
A cool element of GRPO is its versatility. You can use easy rule-based benefit functions-for instance, awarding a reward when the model correctly utilizes the syntax-to guide the training.
While DeepSeek used GRPO, you could utilize alternative methods rather (PPO or PRIME).
For those aiming to dive much deeper, Will Brown has written quite a nice execution of training an LLM with RL using GRPO. GRPO has also currently been included 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 last note on explaining DeepSeek-R1 and the methods they have actually presented in their paper, I want to highlight a passage from the DeepSeekMath paper, based on a point Yannic Kilcher made in his video.
These findings indicate that RL improves the design's general efficiency by rendering the output circulation more robust, simply put, it appears that the improvement is credited to improving the appropriate reaction from TopK instead of the enhancement of basic abilities.
To put it simply, RL fine-tuning tends to shape the output distribution so that the highest-probability outputs are more most likely to be appropriate, even though the general capability (as determined by the variety of right responses) is mainly present in the pretrained design.
This recommends that support learning on LLMs is more about refining and "forming" the existing circulation of responses rather than endowing the model with completely brand-new capabilities.
Consequently, while RL techniques such as PPO and GRPO can produce substantial efficiency gains, there seems an intrinsic ceiling identified by the underlying model's pretrained understanding.
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 have actually used DeepSeek-R1 by means of the main chat interface for numerous problems, which it appears to resolve well enough. The additional search functionality 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 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 perform when deployed on a single H100 GPU-not to thoroughly 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 via llama.cpp:
29 layers seemed to be the sweet spot provided this setup.
Performance:
A r/localllama user explained that they were able to get over 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 totally 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 manageable for any severe work, however it's enjoyable to run these big designs on available hardware.
What matters most to me is a mix of effectiveness and time-to-usefulness in these designs. Since reasoning models need to think before addressing, their time-to-usefulness is usually greater than other designs, however their usefulness is likewise normally higher.
We need 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 shoots up 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: the Limits of Mathematical Reasoning in Open Language Models
DeepSeek R1 - Notion (Building a totally regional "deep researcher" with DeepSeek-R1 - YouTube).
DeepSeek R1's recipe to replicate o1 and the future of reasoning 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 merges 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 presents DeepSeek-R1, an open-source reasoning model that rivals the performance of OpenAI's o1. It presents a detailed method for training such designs using large-scale reinforcement knowing methods.
DeepSeek-V3 Technical Report (December 2024) This report discusses the implementation of an FP8 blended accuracy training framework verified on a very massive design, attaining both accelerated training and reduced GPU memory usage.
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism (January 2024) This paper dives 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 models with a long-lasting 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 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 model characterized by affordable training and effective inference.
DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence (June 2024) This research study presents 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 duplicate R1, fully open source (Jan 25, '25).
- OpenAI researcher validates the DeepSeek team individually discovered and utilized some core ideas the OpenAI team used on the way to o1
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Understanding DeepSeek R1
eugeniatafoya edited this page 2025-02-11 03:48:31 +01:00