DeepSeek-R1 is an open-source language design built on DeepSeek-V3-Base that's been making waves in the AI community. Not just does it match-or even surpass-OpenAI's o1 model in lots of benchmarks, however it likewise comes with completely MIT-licensed weights. This marks it as the first non-OpenAI/Google design to provide strong thinking capabilities in an open and available way.
What makes DeepSeek-R1 particularly exciting is its transparency. Unlike the less-open methods from some industry leaders, DeepSeek has published a detailed training methodology in their paper.
The design is likewise remarkably 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 better designs required more data and compute. While that's still legitimate, models like o1 and R1 demonstrate an alternative: inference-time scaling through reasoning.
The Essentials
The DeepSeek-R1 paper provided several models, however main amongst them were R1 and R1-Zero. Following these are a series of distilled designs that, while interesting, I will not go over here.
DeepSeek-R1 uses two significant 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 approach that counts on comparing several design outputs per timely to prevent the requirement for a separate critic.
R1 and R1-Zero are both reasoning designs. This essentially implies they do Chain-of-Thought before addressing. For the R1 series of designs, this takes kind as thinking within a tag, before responding to with a last summary.
R1-Zero vs R1
R1-Zero applies Reinforcement Learning (RL) straight to DeepSeek-V3-Base with no supervised fine-tuning (SFT). RL is utilized to optimize the model's policy to make the most of benefit.
R1-Zero attains excellent precision however sometimes produces complicated outputs, such as mixing multiple languages in a single action. R1 repairs that by integrating minimal supervised fine-tuning and multiple RL passes, which enhances both correctness and readability.
It is fascinating how some languages might reveal certain ideas better, which leads the design to pick the most meaningful language for the task.
Training Pipeline
The training pipeline that DeepSeek released in the R1 paper is tremendously fascinating. It showcases how they developed such strong thinking models, and what you can get out of each stage. This includes the problems that the resulting designs from each phase have, and how they solved it in the next stage.
It's interesting that their training pipeline differs from the typical:
The normal training strategy: Pretraining on large dataset (train to predict next word) to get the base design → supervised fine-tuning → preference tuning by means of RLHF
R1-Zero: Pretrained → RL
R1: Pretrained → Multistage training pipeline with several SFT and RL phases
Cold-Start Fine-Tuning: Fine-tune DeepSeek-V3-Base on a few thousand Chain-of-Thought (CoT) samples to make sure the RL procedure has a good starting point. This provides an excellent 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 procedure, they relocated to the next action. The result of this action is a strong thinking design but with weak basic capabilities, e.g., poor format and language blending.
Rejection Sampling + basic information: Create new SFT data through rejection sampling on the RL checkpoint (from action 2), combined with monitored data from the DeepSeek-V3-Base design. They collected around 600k high-quality reasoning samples.
Second Fine-Tuning: Fine-tune DeepSeek-V3-Base again on 800k overall samples (600k thinking + 200k basic tasks) for more comprehensive capabilities. This action resulted in a strong reasoning model with general abilities.
Second RL Stage: Add more reward signals (helpfulness, harmlessness) to refine the last model, in addition to the reasoning rewards. The outcome is DeepSeek-R1.
They also did design distillation for numerous Qwen and Llama designs on the reasoning traces to get distilled-R1 designs.
Model distillation is a strategy where you use a teacher design to enhance a trainee design by creating training data for the trainee model.
The instructor is normally a larger model 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 precise and helpful responses.
They utilized a benefit system that inspects not only for correctness but also for appropriate formatting and language consistency, so the design gradually discovers to favor actions that satisfy these quality requirements.
In this paper, they motivate the R1 design to generate chain-of-thought reasoning through RL training with GRPO.
Instead of including a separate module at inference time, the training procedure itself nudges the design to produce detailed, detailed outputs-making the chain-of-thought an emerging habits of the enhanced policy.
What makes their method particularly interesting is its reliance on straightforward, rule-based reward functions.
Instead of depending upon costly external models or human-graded examples as in conventional RLHF, the RL used for chessdatabase.science R1 uses easy criteria: it might provide a higher benefit if the answer is right, if it follows the expected/ format, and if the language of the answer matches that of the prompt.
Not counting on a benefit model also suggests you do not need to hang around 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 prompt, the design produces various reactions.
2. Each action receives a scalar benefit based on aspects like accuracy, formatting, and language consistency.
3. Rewards are adjusted relative to the group's performance, basically measuring 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 small adjustments-using techniques like clipping and a KL penalty-to ensure the policy does not stray too far from its initial behavior.
A cool aspect of GRPO is its flexibility. You can utilize basic rule-based reward functions-for circumstances, granting a reward when the design correctly uses the syntax-to guide the training.
While DeepSeek used GRPO, you might use alternative approaches rather (PPO or PRIME).
For those aiming to dive much deeper, Will Brown has actually written quite a nice 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 great video explaining GRPO by going through the DeepSeekMath paper.
Is RL on LLMs the course to AGI?
As a last note on explaining DeepSeek-R1 and the methods they've 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 show that RL boosts the model's total efficiency by rendering the output circulation more robust, to put it simply, it appears that the improvement is credited to improving the appropriate action from TopK instead of the enhancement of fundamental capabilities.
Simply put, RL fine-tuning tends to shape the output distribution so that the highest-probability outputs are more most likely to be appropriate, videochatforum.ro even though the overall ability (as determined by the diversity of correct responses) is mainly present in the pretrained design.
This suggests that support knowing on LLMs is more about refining and "forming" the existing circulation of actions instead of endowing the design with completely brand-new capabilities.
Consequently, while RL techniques such as PPO and GRPO can produce considerable performance gains, there appears to be a fundamental ceiling figured out 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 big turning point. I'm thrilled to see how it unfolds!
Running DeepSeek-R1
I have actually used DeepSeek-R1 via the main chat interface for numerous problems, which it appears to resolve well enough. The extra search functionality makes it even nicer to use.
Interestingly, o3-mini(-high) was released as I was composing this post. From my initial testing, R1 appears more powerful at math than o3-mini.
I likewise 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 objective was to see how the design would carry out when released on a single H100 GPU-not to extensively test the design's abilities.
671B through 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 through llama.cpp:
29 layers appeared to be the sweet area given 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 gaming setup.
Digital Spaceport composed a full guide on how to run Deepseek R1 671b totally 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 for any major work, however it's fun to run these large models on available hardware.
What matters most to me is a mix of usefulness and time-to-usefulness in these models. Since reasoning designs require to believe before addressing, their time-to-usefulness is normally greater than other designs, but their usefulness is likewise usually greater.
We need to both take full advantage of usefulness and reduce 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 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 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 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 framework that unifies multimodal understanding and generation. It can both comprehend and create images.
DeepSeek-R1: Incentivizing Reasoning Capability in Large Language Models via Reinforcement Learning (January 2025) This paper presents DeepSeek-R1, an open-source reasoning design that matches the efficiency of OpenAI's o1. It presents a detailed method for training such models utilizing large-scale support knowing techniques.
DeepSeek-V3 Technical Report (December 2024) This report discusses the implementation of an FP8 blended accuracy training structure validated on an exceptionally large-scale design, links.gtanet.com.br attaining both sped up training and reduced GPU memory use.
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism (January 2024) This paper looks into scaling laws and presents findings that help with the scaling of massive models in open-source setups. It introduces the DeepSeek LLM job, dedicated 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 introduces 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 use a fill-in-the-blank task to enhance code generation and infilling.
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model (May 2024) This paper presents DeepSeek-V2, a Mixture-of-Experts (MoE) language model characterized by cost-effective 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 model that attains performance comparable to GPT-4 Turbo in code-specific jobs.
Interesting occasions
- Hong Kong University duplicates R1 results (Jan 25, '25).
- Huggingface reveals huggingface/open-r 1: Fully open recreation of DeepSeek-R1 to duplicate R1, fully open source (Jan 25, '25).
- OpenAI researcher verifies the DeepSeek team separately found and used some core ideas the OpenAI team used on the method to o1
Liked this post? Join the newsletter.