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 standards, however it likewise includes completely MIT-licensed weights. This marks it as the first non-OpenAI/Google model to deliver strong thinking capabilities in an open and available way.
What makes DeepSeek-R1 especially interesting is its transparency. Unlike the less-open techniques from some market leaders, DeepSeek has released a detailed training methodology in their paper.
The design is also 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 much better models required more data and compute. While that's still valid, models like o1 and R1 demonstrate an option: inference-time scaling through thinking.
The Essentials
The DeepSeek-R1 paper presented multiple designs, however main among them were R1 and R1-Zero. Following these are a series of distilled models that, while interesting, I won't go over here.
DeepSeek-R1 uses 2 major ideas:
1. A multi-stage pipeline where a small set of cold-start information kickstarts the design, followed by massive RL.
2. Group Relative Policy Optimization (GRPO), a support knowing approach that relies on comparing multiple model outputs per prompt to avoid the requirement for a separate critic.
R1 and R1-Zero are both reasoning designs. This basically indicates they do Chain-of-Thought before addressing. For the R1 series of designs, this takes type as thinking within a tag, before answering with a final summary.
R1-Zero vs R1
R1-Zero applies Reinforcement Learning (RL) straight to DeepSeek-V3-Base without any monitored fine-tuning (SFT). RL is used to enhance the design's policy to optimize reward.
R1-Zero attains outstanding precision but sometimes produces confusing outputs, such as mixing multiple languages in a single response. R1 repairs that by integrating restricted monitored fine-tuning and several RL passes, which enhances both correctness and readability.
It is interesting how some languages might express certain concepts much better, which leads the model to pick the most expressive language for the task.
Training Pipeline
The training pipeline that DeepSeek published in the R1 paper is exceptionally fascinating. It showcases how they developed 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 stage.
It's fascinating that their training pipeline differs from the typical:
The usual training technique: Pretraining on big dataset (train to anticipate next word) to get the base design → monitored fine-tuning → choice tuning through RLHF
R1-Zero: Pretrained → RL
R1: Pretrained → Multistage training pipeline with several SFT and RL stages
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 decent starting point. This provides a good design to begin RL.
First RL Stage: Apply GRPO with rule-based benefits to enhance reasoning accuracy and format (such as forcing chain-of-thought into believing tags). When they were near convergence in the RL process, they moved to the next action. The result of this action is a strong thinking model but with weak general capabilities, e.g., bad format and language blending.
Rejection Sampling + general information: Create brand-new SFT data through rejection tasting on the RL checkpoint (from action 2), combined with monitored information from the DeepSeek-V3-Base model. They gathered 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 abilities. This step resulted in a strong reasoning model with general abilities.
Second RL Stage: Add more benefit signals (helpfulness, harmlessness) to refine the last design, in addition to the reasoning benefits. The result is DeepSeek-R1.
They also did model distillation for numerous Qwen and Llama designs on the reasoning traces to get distilled-R1 models.
Model distillation is a technique where you utilize a to enhance a trainee model by generating training data for the trainee model.
The teacher is generally a larger design than the trainee.
Group Relative Policy Optimization (GRPO)
The fundamental idea behind using support learning for LLMs is to tweak the model's policy so that it naturally produces more accurate and beneficial answers.
They used a benefit system that inspects not just for accuracy but likewise for proper formatting and language consistency, so the model slowly finds out to prefer actions that meet these quality criteria.
In this paper, they motivate the R1 model to create chain-of-thought thinking through RL training with GRPO.
Rather than including a separate module at reasoning time, the training procedure itself pushes the design to produce detailed, detailed outputs-making the chain-of-thought an emergent behavior of the optimized policy.
What makes their method especially intriguing is its reliance on straightforward, rule-based reward functions.
Instead of depending on pricey external designs or human-graded examples as in traditional RLHF, the RL utilized for R1 utilizes easy criteria: it might provide a greater reward if the answer is appropriate, if it follows the anticipated/ format, and if the language of the response matches that of the prompt.
Not relying on a benefit model likewise indicates you do not need to hang out and effort training it, and it does not take memory and compute far from your main design.
GRPO was introduced in the DeepSeekMath paper. Here's how GRPO works:
1. For each input prompt, the design creates different reactions.
2. Each response receives a scalar benefit based on aspects like precision, formatting, and language consistency.
3. Rewards are changed relative to the group's efficiency, basically measuring how much better each action is compared to the others.
4. The model updates its strategy a little to prefer actions with greater relative benefits. It only makes minor adjustments-using strategies like clipping and a KL penalty-to guarantee the policy doesn't wander off too far from its original behavior.
A cool aspect of GRPO is its versatility. You can use simple rule-based benefit functions-for instance, awarding a benefit when the design correctly uses the syntax-to guide the training.
While DeepSeek utilized GRPO, you could utilize alternative techniques instead (PPO or PRIME).
For those aiming to dive much deeper, Will Brown has actually composed quite a good implementation of training an LLM with RL utilizing GRPO. GRPO has actually also currently been included to the Transformer Reinforcement Learning (TRL) library, which is another great resource.
Finally, Yannic Kilcher has a fantastic 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 approaches they've provided 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 boosts the design's overall efficiency by rendering the output distribution more robust, in other words, it appears that the improvement is credited to boosting the appropriate reaction from TopK instead of the enhancement of fundamental abilities.
Simply put, RL fine-tuning tends to shape the output circulation so that the highest-probability outputs are most likely to be right, even though the overall capability (as determined by the variety of correct responses) is mainly present in the pretrained model.
This suggests that reinforcement knowing on LLMs is more about refining and "shaping" the existing circulation of responses instead of enhancing the design with entirely new capabilities.
Consequently, while RL methods such as PPO and GRPO can produce significant efficiency gains, there seems an intrinsic ceiling determined by the underlying design'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 user interface for numerous issues, which it seems to resolve all right. The extra search performance makes it even nicer to utilize.
Interestingly, o3-mini(-high) was released as I was composing this post. From my preliminary testing, R1 seems stronger at mathematics than o3-mini.
I likewise leased 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 design would carry out when released on a single H100 GPU-not to thoroughly evaluate the model's abilities.
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 working 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 using their GPU on their local 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 rather manageable for any major work, but it's enjoyable to run these big designs on available hardware.
What matters most to me is a combination of usefulness and time-to-usefulness in these designs. Since reasoning models need to think before answering, their time-to-usefulness is generally higher than other designs, but their usefulness is likewise usually greater.
We require to both take full advantage of usefulness and reduce time-to-usefulness.
70B by means of 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 via Reinforcement Learning
[2402.03300] DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
DeepSeek R1 - Notion (Building a totally regional "deep scientist" with DeepSeek-R1 - YouTube).
DeepSeek R1's recipe to reproduce 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 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 structure that merges multimodal understanding and generation. It can both understand and produce images.
DeepSeek-R1: Incentivizing Reasoning Capability in Large Language Models through Reinforcement Learning (January 2025) This paper introduces DeepSeek-R1, an open-source reasoning model that rivals the efficiency of OpenAI's o1. It provides a detailed method for training such designs utilizing large-scale reinforcement learning strategies.
DeepSeek-V3 Technical Report (December 2024) This report talks about the application of an FP8 mixed precision training framework validated on an extremely massive model, attaining both sped up training and decreased GPU memory use.
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism (January 2024) This paper digs into scaling laws and presents findings that help with the scaling of large-scale models in open-source configurations. It introduces the DeepSeek LLM task, devoted to advancing open-source language designs with a long-term viewpoint.
DeepSeek-Coder: When the Large Language Model Meets Programming-The Rise of Code Intelligence (January 2024) This research study presents the DeepSeek-Coder series, a range 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 employ a fill-in-the-blank task to improve code generation and infilling.
DeepSeek-V2: demo.qkseo.in 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 cost-effective training and effective 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 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 duplicate R1, fully open source (Jan 25, '25).
- OpenAI researcher verifies the DeepSeek team independently discovered and utilized some core ideas the OpenAI group used en route to o1
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