Understanding DeepSeek R1

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 standards, however it likewise features totally MIT-licensed weights. This marks it as the very first non-OpenAI/Google design to provide strong reasoning abilities in an open and available way.

What makes DeepSeek-R1 especially amazing 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 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 much better models needed more data and calculate. While that’s still legitimate, models like o1 and R1 demonstrate an option: inference-time scaling through thinking.

The Essentials

The DeepSeek-R1 paper provided numerous models, but main amongst them were R1 and R1-Zero. Following these are a series of distilled models that, while interesting, I will not discuss here.

DeepSeek-R1 uses 2 significant ideas:

1. A multi-stage pipeline where a small set of cold-start information kickstarts the model, followed by massive RL.
2. Group Relative Policy Optimization (GRPO), a reinforcement learning technique that counts on comparing several model outputs per prompt to prevent the need for a separate critic.

R1 and R1-Zero are both thinking models. This essentially means they do Chain-of-Thought before responding to. For the R1 series of models, this takes form as thinking within a tag, before addressing with a final summary.

R1-Zero vs R1

R1-Zero applies Reinforcement Learning (RL) straight to DeepSeek-V3-Base without any supervised fine-tuning (SFT). RL is used to optimize the model’s policy to make the most of benefit.
R1-Zero attains exceptional accuracy but in some cases produces confusing outputs, such as blending several languages in a single response. R1 repairs that by incorporating limited monitored fine-tuning and numerous RL passes, which improves both accuracy and readability.

It is fascinating how some languages may express certain concepts better, which leads the design to choose the most expressive language for the job.

Training Pipeline

The training pipeline that DeepSeek released in the R1 paper is tremendously fascinating. It showcases how they developed such strong reasoning designs, and what you can anticipate from each stage. This includes the problems that the resulting designs from each stage have, and how they fixed it in the next stage.

It’s interesting that their training pipeline varies from the usual:

The normal training method: Pretraining on big dataset (train to anticipate next word) to get the base design → monitored fine-tuning → preference tuning via 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 couple of thousand Chain-of-Thought (CoT) samples to ensure the RL procedure has a good beginning point. This gives an excellent design to begin RL.
First RL Stage: Apply GRPO with rule-based rewards to enhance reasoning correctness and formatting (such as forcing chain-of-thought into thinking tags). When they were near convergence in the RL procedure, they transferred to the next action. The result of this action is a strong thinking model but with weak general abilities, e.g., bad format and language blending.
Rejection Sampling + basic information: Create brand-new SFT information through rejection tasting on the RL checkpoint (from action 2), integrated with monitored data from the DeepSeek-V3-Base design. They collected around 600k premium reasoning samples.
Second Fine-Tuning: Fine-tune DeepSeek-V3-Base again on 800k total samples (600k thinking + 200k general tasks) for broader capabilities. This action led to a strong reasoning model with basic abilities.
Second RL Stage: Add more reward signals (helpfulness, library.kemu.ac.ke harmlessness) to refine the final model, in addition to the thinking rewards. The result is DeepSeek-R1.
They likewise 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 use a teacher model to improve a trainee design by producing training information for the trainee model.
The teacher is typically a bigger model than the trainee.

Group Relative Policy Optimization (GRPO)

The standard idea behind using support knowing for LLMs is to tweak the model’s policy so that it naturally produces more accurate and helpful answers.
They utilized a reward system that checks not only for accuracy however also for proper format and language consistency, so the model slowly discovers to favor actions that fulfill these quality requirements.

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

What makes their method especially intriguing is its reliance on straightforward, pipewiki.org rule-based reward functions.
Instead of depending upon expensive external designs or human-graded examples as in traditional RLHF, the RL used for wiki.monnaie-libre.fr R1 utilizes simple requirements: it may provide a higher reward if the response is correct, dokuwiki.stream if it follows the anticipated/ format, and if the language of the answer matches that of the prompt.
Not relying on a reward model likewise means you do not have to hang around and effort training it, and it doesn’t 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 timely, the model produces different responses.
2. Each reaction gets a scalar benefit based upon elements like precision, formatting, and language consistency.
3. Rewards are changed relative to the group’s performance, humanlove.stream basically determining just how much better each response is compared to the others.
4. The model updates its technique slightly to favor reactions with greater relative benefits. It just makes small adjustments-using methods like clipping and a KL penalty-to make sure the policy does not wander off too far from its original habits.

A cool element of GRPO is its versatility. You can use easy rule-based benefit functions-for circumstances, granting a bonus offer when the model properly utilizes the syntax-to guide the training.

While DeepSeek used GRPO, you could use alternative approaches instead (PPO or PRIME).

For those aiming to dive deeper, Will Brown has composed quite a great implementation 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 an excellent video explaining GRPO by going through the DeepSeekMath paper.

Is RL on LLMs the course to AGI?

As a final note on explaining DeepSeek-R1 and the approaches they have actually 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 improves the design’s total efficiency by rendering the output circulation more robust, to put it simply, it appears that the improvement is attributed to boosting the appropriate from TopK rather than the improvement of basic abilities.

To put it simply, RL fine-tuning tends to form the output distribution so that the highest-probability outputs are most likely to be right, even though the general ability (as measured by the variety of right answers) is mainly present in the pretrained model.

This recommends that support knowing on LLMs is more about refining and „shaping“ the existing distribution of actions rather than endowing the design with entirely new capabilities.
Consequently, while RL techniques such as PPO and GRPO can produce significant efficiency gains, there seems an intrinsic 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 milestone. I’m excited to see how it unfolds!

Running DeepSeek-R1

I’ve utilized DeepSeek-R1 through the main chat user interface for numerous problems, which it appears to resolve all right. The additional search performance makes it even nicer to use.

Interestingly, o3-mini(-high) was launched as I was composing this post. From my preliminary testing, R1 seems more powerful at mathematics 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 objective was to see how the model would carry out when deployed on a single H100 GPU-not to extensively test the model’s capabilities.

671B by means of 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), asteroidsathome.net running by means of llama.cpp:

29 layers appeared to be the sweet spot provided this setup.

Performance:

A r/localllama user explained that they had the ability to overcome 2 tok/sec with DeepSeek R1 671B, without using their GPU on their regional gaming setup.
Digital Spaceport wrote a complete 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 severe work, but 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, however their usefulness is likewise typically greater.
We need to both maximize effectiveness and minimize time-to-usefulness.

70B by means of Ollama

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

GPU usage shoots up 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 totally 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: 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 framework that unifies multimodal understanding and generation. It can both understand and create images.
DeepSeek-R1: Incentivizing Reasoning Capability in Large Language Models by means of Reinforcement Learning (January 2025) This paper presents DeepSeek-R1, an open-source reasoning model that measures up to the efficiency of OpenAI’s o1. It provides a detailed method for training such models utilizing large-scale reinforcement learning methods.
DeepSeek-V3 Technical Report (December 2024) This report talks about the application of an FP8 blended accuracy training structure confirmed on a very large-scale design, attaining both accelerated training and lowered GPU memory usage.
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 models in open-source configurations. It introduces the DeepSeek LLM project, committed to advancing open-source language designs with a long-term perspective.
DeepSeek-Coder: When the Large Language Model Meets Programming-The Rise of Code Intelligence (January 2024) This research presents the DeepSeek-Coder series, a series of open-source code designs trained from scratch on 2 trillion tokens. The models are pre-trained on a premium project-level code corpus and utilize a fill-in-the-blank task to boost 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 affordable 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 design that attains efficiency equivalent to GPT-4 Turbo in code-specific tasks.

Interesting occasions

– Hong Kong University duplicates R1 outcomes (Jan 25, ’25).
– Huggingface reveals huggingface/open-r 1: Fully open recreation of DeepSeek-R1 to reproduce R1, fully open source (Jan 25, ’25).
– OpenAI scientist confirms the DeepSeek group separately discovered and utilized some core ideas the OpenAI group used en route to o1

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