DeepSeek-R1 is an open-source language model constructed 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 many benchmarks, but it also features fully MIT-licensed weights. This marks it as the first non-OpenAI/Google design to provide 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 market leaders, DeepSeek has published a detailed training method in their paper.
The design 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 typical knowledge was that better designs needed more information and compute. While that's still legitimate, models like o1 and R1 demonstrate an option: inference-time scaling through reasoning.
The Essentials
The DeepSeek-R1 paper provided multiple models, but main amongst 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 two major concepts:
1. A multi-stage pipeline where a little set of cold-start data kickstarts the design, followed by large-scale RL.
2. Group Relative Policy Optimization (GRPO), a support knowing method 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 basically means they do Chain-of-Thought before responding to. For the R1 series of models, this takes kind 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 make the most of reward.
R1-Zero attains excellent precision but in some cases produces complicated outputs, such as blending multiple languages in a single reaction. R1 repairs that by incorporating minimal monitored fine-tuning and several RL passes, which enhances both accuracy and readability.
It is interesting how some languages might reveal certain concepts much better, which leads the design to select the most meaningful language for the task.
Training Pipeline
The training pipeline that DeepSeek released in the R1 paper is profoundly fascinating. It showcases how they produced such strong reasoning designs, and what you can get out of each phase. This includes the problems that the resulting models from each stage have, and how they fixed it in the next phase.
It's fascinating that their training pipeline differs from the normal:
The normal training method: Pretraining on large dataset (train to predict next word) to get the base design → monitored fine-tuning → preference tuning through 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 couple of thousand Chain-of-Thought (CoT) samples to guarantee the RL procedure has a good beginning point. This gives an excellent 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 procedure, they transferred to the next action. The outcome of this action is a strong reasoning model but with weak basic capabilities, e.g., bad formatting and language blending.
Rejection Sampling + general data: Create new SFT information through rejection tasting on the RL checkpoint (from action 2), combined 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 overall samples (600k thinking + 200k general tasks) for broader capabilities. This step led to a strong reasoning design with general capabilities.
Second RL Stage: Add more benefit signals (helpfulness, harmlessness) to fine-tune the final design, in addition to the thinking rewards. The result is DeepSeek-R1.
They also did model distillation for several Qwen and Llama models on the reasoning traces to get distilled-R1 models.
Model distillation is a technique where you use a teacher model to improve a trainee design by producing training information for the trainee design.
The instructor is generally a larger design than the trainee.
Group Relative Policy Optimization (GRPO)
The standard concept behind utilizing reinforcement learning for LLMs is to fine-tune the model's policy so that it naturally produces more precise and useful responses.
They utilized a reward system that examines not just for accuracy however also for appropriate format and language consistency, so the design gradually discovers to prefer actions that satisfy these quality requirements.
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 inference time, the training process itself pushes the model to produce detailed, detailed outputs-making the chain-of-thought an emerging habits of the enhanced policy.
What makes their technique especially intriguing is its reliance on straightforward, rule-based reward functions.
Instead of depending upon expensive external designs or human-graded examples as in traditional RLHF, the RL used for R1 utilizes 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 timely.
Not depending on a reward model likewise implies you do not need to hang around and effort training it, and it does not 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 creates different reactions.
2. Each action receives a scalar benefit based on elements like precision, formatting, and language consistency.
3. Rewards are adjusted relative to the group's efficiency, essentially determining how much better each action is compared to the others.
4. The design updates its method slightly to prefer responses with greater relative benefits. It only makes small adjustments-using methods like clipping and a KL penalty-to make sure the policy doesn't stray too far from its original habits.
A cool aspect of GRPO is its versatility. You can use basic rule-based benefit functions-for circumstances, a perk when the design properly utilizes the syntax-to guide the training.
While DeepSeek utilized GRPO, you could use alternative approaches rather (PPO or PRIME).
For those aiming to dive much deeper, Will Brown has written quite a great application of training an LLM with RL using GRPO. GRPO has likewise currently been contributed 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 methodologies they've 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 indicate that RL boosts the design's general performance by rendering the output circulation more robust, simply put, it seems that the enhancement is credited to increasing the correct reaction from TopK rather than the improvement of fundamental abilities.
To put it simply, RL fine-tuning tends to form the output circulation so that the highest-probability outputs are more likely to be right, despite the fact that the total capability (as measured by the diversity of right answers) is mainly present in the pretrained design.
This recommends that reinforcement knowing on LLMs is more about refining and "shaping" the existing distribution of actions rather than enhancing the design with completely brand-new capabilities.
Consequently, while RL methods such as PPO and GRPO can produce substantial performance gains, there seems an intrinsic ceiling figured out 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 delighted to see how it unfolds!
Running DeepSeek-R1
I have actually used DeepSeek-R1 through the main chat interface for various problems, which it seems to resolve well enough. The extra search functionality makes it even better to utilize.
Interestingly, o3-mini(-high) was launched as I was composing this post. From my preliminary screening, R1 seems stronger at math than o3-mini.
I also leased a single H100 via 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 design would carry out when deployed on a single H100 GPU-not to thoroughly check the model's abilities.
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 working on the GPU), running by means of llama.cpp:
29 layers appeared to be the sweet area offered this configuration.
Performance:
A r/localllama user explained that they had the ability to get over 2 tok/sec with DeepSeek R1 671B, without using their GPU on their regional video gaming setup.
Digital Spaceport composed a full guide on how to run Deepseek R1 671b fully 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 major work, however it's enjoyable to run these big 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 effectiveness is also usually 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 by means of Ollama:
GPU usage 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 via Reinforcement Learning
[2402.03300] DeepSeekMath: Pushing 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 grandma - 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 structure that merges multimodal understanding and generation. It can both comprehend and produce images.
DeepSeek-R1: Incentivizing Reasoning Capability in Large Language Models via Reinforcement Learning (January 2025) This paper introduces DeepSeek-R1, an open-source reasoning design that rivals the performance of OpenAI's o1. It presents a detailed approach for training such models utilizing massive support knowing strategies.
DeepSeek-V3 Technical Report (December 2024) This report talks about the implementation of an FP8 combined precision training structure verified on a very large-scale design, attaining both sped up training and minimized GPU memory use.
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism (January 2024) This paper looks into scaling laws and provides findings that help with the scaling of massive designs in open-source configurations. It presents the DeepSeek LLM task, dedicated 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 variety of open-source code designs trained from scratch on 2 trillion tokens. The models are pre-trained on a high-quality project-level code corpus and employ a fill-in-the-blank job to improve 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 defined by cost-effective training and effective inference.
DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence (June 2024) This research presents 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 occasions
- Hong Kong University duplicates R1 outcomes (Jan 25, '25).
- Huggingface reveals huggingface/open-r 1: Fully open reproduction of DeepSeek-R1 to replicate R1, fully open source (Jan 25, '25).
- OpenAI scientist verifies the DeepSeek group individually found and forum.altaycoins.com used some core ideas the OpenAI team used en route to o1
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