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 design in numerous criteria, but it likewise features completely MIT-licensed weights. This marks it as the very first non-OpenAI/Google model to provide strong reasoning capabilities in an open and available way.

What makes DeepSeek-R1 especially exciting is its openness. Unlike the less-open approaches from some market leaders, DeepSeek has released a detailed training method in their paper.
The model is likewise incredibly economical, 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 better models required more data and compute. While that's still legitimate, designs like o1 and R1 show an alternative: inference-time scaling through thinking.

The Essentials

The DeepSeek-R1 paper presented numerous designs, but main amongst them were R1 and R1-Zero. Following these are a series of distilled models that, while intriguing, I will not go over here.

DeepSeek-R1 uses 2 major ideas:

1. A multi-stage pipeline where a little set of cold-start data kickstarts the model, followed by large-scale RL.
2. Group Relative Policy Optimization (GRPO), a support learning technique that counts on comparing numerous design outputs per prompt to prevent the need for a different critic.

R1 and R1-Zero are both reasoning models. This essentially suggests they do Chain-of-Thought before answering. For the R1 series of models, this takes form as believing within a tag, before answering with a final summary.

R1-Zero vs R1

R1-Zero applies Reinforcement Learning (RL) straight to DeepSeek-V3-Base with no monitored fine-tuning (SFT). RL is utilized to optimize the design's policy to maximize benefit.
R1-Zero attains exceptional precision however in some cases produces confusing outputs, such as mixing numerous languages in a single reaction. R1 repairs that by including restricted monitored fine-tuning and several RL passes, which enhances both correctness and readability.

It is interesting how some languages might reveal certain ideas better, which leads the model to select the most expressive language for the task.

Training Pipeline

The training pipeline that DeepSeek released in the R1 paper is immensely fascinating. It showcases how they created such strong reasoning designs, and what you can get out of each stage. This consists of the problems that the resulting models from each stage have, and how they solved it in the next phase.

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

The typical training strategy: Pretraining on big dataset (train to forecast next word) to get the base model → monitored fine-tuning → choice 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 couple of thousand Chain-of-Thought (CoT) samples to make sure the RL procedure has a decent starting point. This provides a great design to start RL.
First RL Stage: Apply GRPO with rule-based benefits to enhance reasoning accuracy and formatting (such as requiring chain-of-thought into thinking tags). When they were near convergence in the RL process, they relocated to the next step. The result of this action is a strong thinking design but with weak basic abilities, e.g., poor formatting and language mixing.
Rejection Sampling + basic data: Create brand-new SFT information through rejection tasting on the RL checkpoint (from step 2), integrated with monitored data from the DeepSeek-V3-Base model. They collected around 600k premium thinking samples.
Second Fine-Tuning: Fine-tune DeepSeek-V3-Base again on 800k overall samples (600k thinking + 200k general jobs) for wider capabilities. This action resulted in a strong reasoning design with general abilities.
Second RL Stage: Add more reward signals (helpfulness, harmlessness) to refine 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 thinking traces to get distilled-R1 designs.

Model distillation is a strategy where you utilize a teacher design to improve a trainee design by generating training information for the trainee model.
The instructor is usually a bigger design than the trainee.

Group Relative Policy Optimization (GRPO)

The fundamental idea behind utilizing reinforcement learning for LLMs is to tweak the design's policy so that it naturally produces more accurate and beneficial answers.
They used a benefit system that examines not just for accuracy but also for proper formatting and language consistency, so the design slowly learns to favor actions that satisfy these quality criteria.

In this paper, they motivate the R1 design to create chain-of-thought thinking through RL training with GRPO.
Instead of including a separate module at reasoning time, the training procedure itself pushes the model to produce detailed, detailed outputs-making the chain-of-thought an emerging habits of the optimized policy.

What makes their approach particularly fascinating is its dependence on straightforward, rule-based benefit functions.
Instead of depending on costly external models 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 anticipated/ formatting, and if the language of the answer matches that of the timely.
Not relying on a benefit design also means you do not need to hang out and effort training it, and it doesn't take memory and compute far from your main model.

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

1. For each input prompt, the design generates various actions.
2. Each reaction receives a scalar reward based upon factors like precision, formatting, 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 design updates its technique somewhat to favor responses with higher relative benefits. It just makes slight adjustments-using techniques like clipping and a KL penalty-to ensure the policy doesn't stray too far from its original behavior.

A cool element of GRPO is its versatility. You can utilize basic rule-based reward functions-for circumstances, granting a bonus offer when the design correctly utilizes the syntax-to guide the training.

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

For those aiming to dive deeper, Will Brown has composed quite a great application of training an LLM with RL using GRPO. GRPO has also already 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 approaches they have actually presented in their paper, I wish 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 performance by rendering the output distribution more robust, to put it simply, it seems that the enhancement is credited to improving the correct reaction from TopK instead of the enhancement of fundamental capabilities.

Simply put, RL fine-tuning tends to form the output circulation so that the highest-probability outputs are most likely to be right, despite the fact that the total capability (as measured by the variety of right answers) is mainly present in the pretrained model.

This recommends that reinforcement knowing on LLMs is more about refining and "forming" the existing circulation of responses rather than enhancing the model with entirely new capabilities.
Consequently, while RL techniques such as PPO and ai-db.science GRPO can produce considerable performance gains, there appears to be a fundamental 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've used DeepSeek-R1 through the main chat user interface for numerous problems, which it seems to resolve all right. The additional search performance makes it even better to use.

Interestingly, o3-mini(-high) was launched as I was composing this post. From my initial testing, R1 appears stronger at math than o3-mini.

I also leased 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 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 appeared to be the sweet spot offered this setup.

Performance:

A r/localllama user explained that they had the ability to get over 2 tok/sec with DeepSeek R1 671B, without utilizing their GPU on their regional gaming setup.
Digital Spaceport wrote a full 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 rather bearable for any major work, but it's fun to run these big designs on available hardware.

What matters most to me is a combination of usefulness and time-to-usefulness in these models. Since reasoning designs require to believe before responding to, their time-to-usefulness is generally greater than other models, but their effectiveness is likewise generally greater.
We require to both maximize usefulness and minimize time-to-usefulness.

70B via Ollama

70.6 b params, 4-bit KM quantized DeepSeek-R1 running via Ollama:

GPU usage 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 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 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 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 a novel autoregressive framework that merges multimodal understanding and generation. It can both comprehend and galgbtqhistoryproject.org 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 design that equals the performance of OpenAI's o1. It provides a detailed approach for training such models utilizing large-scale reinforcement learning strategies.
DeepSeek-V3 Technical Report (December 2024) This report talks about the application of an FP8 combined accuracy training structure validated on a very massive design, attaining both accelerated training and reduced 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 models in open-source setups. It introduces the DeepSeek LLM job, 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 introduces 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 high-quality project-level code corpus and utilize 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 characterized by economical training and efficient 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 model that attains efficiency equivalent to GPT-4 Turbo in code-specific jobs.

Interesting events

- Hong Kong University replicates R1 outcomes (Jan 25, '25).
- Huggingface announces huggingface/open-r 1: Fully open recreation of DeepSeek-R1 to reproduce R1, totally open source (Jan 25, '25).
- OpenAI researcher validates the DeepSeek team separately discovered and used some core concepts the OpenAI group used en route to o1

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