Understanding DeepSeek R1

DeepSeek-R1 is an open-source language design 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 standards, but it also comes with totally MIT-licensed weights. This marks it as the first non-OpenAI/Google design to deliver strong reasoning capabilities in an open and available manner.

What makes DeepSeek-R1 especially interesting is its openness. Unlike the less-open techniques from some industry leaders, DeepSeek has actually released a detailed training method in their paper.
The model is likewise extremely affordable, 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 wisdom was that better models required more information 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 provided multiple designs, however main amongst them were R1 and R1-Zero. Following these are a series of distilled models that, while intriguing, I won’t talk about here.

DeepSeek-R1 uses 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 learning approach that relies on comparing multiple design outputs per prompt to prevent the requirement for a separate critic.

R1 and R1-Zero are both thinking models. This basically means they do Chain-of-Thought before addressing. For the R1 series of designs, this takes kind as believing 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 used to optimize the model’s policy to maximize benefit.
R1-Zero attains exceptional precision however sometimes produces complicated outputs, such as mixing multiple languages in a single reaction. R1 repairs that by integrating limited monitored fine-tuning and numerous RL passes, which improves both accuracy and readability.

It is interesting how some languages may reveal certain ideas much better, which leads the design to select the most meaningful language for the job.

Training Pipeline

The training pipeline that DeepSeek published in the R1 paper is tremendously interesting. It showcases how they developed such strong thinking designs, and what you can anticipate from each stage. This consists of the problems that the resulting models from each phase have, and how they solved it in the next phase.

It’s intriguing that their training pipeline varies from the normal:

The typical training strategy: Pretraining on big dataset (train to predict next word) to get the base design → monitored fine-tuning → choice tuning through RLHF
R1-Zero: Pretrained → RL
R1: Pretrained → Multistage training pipeline with multiple 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 process has a decent beginning point. This offers a good design to start RL.
First RL Stage: Apply GRPO with rule-based rewards to improve reasoning accuracy and format (such as requiring chain-of-thought into thinking tags). When they were near merging in the RL procedure, they moved to the next step. The result of this action is a strong thinking design but with weak basic capabilities, e.g., bad formatting and language mixing.
Rejection Sampling + basic information: Create new SFT data through rejection sampling on the RL checkpoint (from step 2), wolvesbaneuo.com combined with supervised data from the DeepSeek-V3-Base model. They collected around 600k top quality thinking samples.
Second Fine-Tuning: Fine-tune DeepSeek-V3-Base again on 800k overall samples (600k thinking + 200k basic tasks) for more comprehensive capabilities. This step led to a strong thinking model with basic abilities.
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 design distillation for numerous Qwen and Llama models on the thinking traces to get distilled-R1 models.

Model distillation is a method where you utilize a teacher design to improve a trainee model by creating training information for the trainee design.
The teacher is normally a bigger model than the trainee.

Group Relative Policy Optimization (GRPO)

The basic concept behind utilizing reinforcement knowing for LLMs is to tweak the model’s policy so that it naturally produces more precise and beneficial answers.
They utilized a reward system that examines not just for correctness however likewise for correct formatting and language consistency, so the model gradually discovers to favor reactions that satisfy these quality requirements.

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

What makes their technique especially fascinating is its reliance on straightforward, rule-based reward functions.
Instead of depending on costly external models or human-graded examples as in conventional RLHF, the RL utilized for R1 uses easy requirements: it may provide a higher benefit if the answer is correct, if it follows the expected/ formatting, and if the language of the response matches that of the timely.
Not relying on a reward model likewise indicates you do not have to hang around and effort training it, and it does not take memory and compute away from your main design.

GRPO was presented in the DeepSeekMath paper. Here’s how GRPO works:

1. For each input prompt, the design creates various reactions.
2. Each response gets a scalar reward based on factors like precision, formatting, bbarlock.com and language consistency.
3. Rewards are changed relative to the group’s performance, essentially determining how much better each response is compared to the others.
4. The design updates its strategy a little to prefer responses with higher relative advantages. It just makes slight adjustments-using techniques like clipping and a KL penalty-to guarantee the policy doesn’t wander off too far from its initial habits.

A cool element of GRPO is its versatility. You can use basic rule-based benefit functions-for instance, awarding a perk when the design properly uses the syntax-to guide the training.

While DeepSeek used GRPO, you might utilize alternative techniques rather (PPO or PRIME).

For those aiming to dive much deeper, Will Brown has written rather a good execution of training an LLM with RL using GRPO. GRPO has also already been contributed to the Transformer Reinforcement Learning (TRL) library, which is another great 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 final 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 upon a point Yannic Kilcher made in his video.

These findings show that RL enhances the model’s general efficiency by rendering the output distribution more robust, simply put, it seems that the improvement is associated to increasing the right reaction from TopK rather than the improvement of basic capabilities.

In other words, RL fine-tuning tends to shape the output distribution so that the highest-probability outputs are most likely to be right, despite the fact that the overall ability (as measured by the variety of proper 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 reactions rather than enhancing the model with completely new abilities.
Consequently, while RL methods such as PPO and GRPO can produce significant performance gains, there seems an inherent 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 excited to see how it unfolds!

Running DeepSeek-R1

I’ve used DeepSeek-R1 via the main chat user interface for numerous problems, which it seems to fix well enough. The additional search performance makes it even nicer to utilize.

Interestingly, o3-mini(-high) was launched as I was writing this post. From my preliminary testing, R1 seems stronger at math than o3-mini.

I likewise rented 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 objective was to see how the model would perform when deployed on a single H100 GPU-not to thoroughly check the design’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 running on the GPU), running via llama.cpp:

29 layers appeared to be the sweet spot given 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 regional video gaming setup.
Digital Spaceport composed a full guide on how to run Deepseek R1 671b completely 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 bearable for any severe work, however 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 models. Since reasoning models require to believe before addressing, their time-to-usefulness is normally greater than other models, but their effectiveness is likewise usually higher.
We require to both optimize usefulness and minimize time-to-usefulness.

70B by means of Ollama

70.6 b params, 4-bit KM quantized DeepSeek-R1 running through 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 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 reproduce o1 and the future of thinking LMs.
The Illustrated DeepSeek-R1 – by Jay Alammar.
Explainer: drapia.org 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 comprehend and create images.
DeepSeek-R1: Incentivizing Reasoning Capability in Large Language Models via Reinforcement Learning (January 2025) This paper introduces DeepSeek-R1, an open-source reasoning model that rivals the efficiency of OpenAI’s o1. It presents a detailed method for training such models using large-scale support knowing techniques.
DeepSeek-V3 Technical Report (December 2024) This report discusses the application of an FP8 combined precision training structure verified on an incredibly massive model, attaining both sped up training and minimized GPU memory use.
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism (January 2024) This paper explores scaling laws and provides findings that assist in the scaling of massive designs in open-source setups. It introduces the DeepSeek LLM task, committed to advancing open-source language models with a long-term viewpoint.
DeepSeek-Coder: When the Large Language Model Meets Programming-The Rise of Code Intelligence (January 2024) This research introduces the DeepSeek-Coder series, bryggeriklubben.se a series of open-source code models trained from scratch on 2 trillion tokens. The designs are pre-trained on a top quality project-level code corpus and use a fill-in-the-blank job to boost 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 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 introduces DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that attains efficiency similar to GPT-4 Turbo in code-specific tasks.

Interesting occasions

– Hong Kong University replicates R1 results (Jan 25, ’25).
– Huggingface reveals huggingface/open-r 1: Fully open recreation of DeepSeek-R1 to duplicate R1, completely open source (Jan 25, ’25).
– OpenAI researcher confirms the DeepSeek team individually discovered and utilized some core ideas the OpenAI team used on the method to o1

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