DeepSeek-R1 is an open-source language design built on DeepSeek-V3-Base that's been making waves in the AI neighborhood. Not just does it match-or even surpass-OpenAI's o1 design in numerous standards, however it also includes completely MIT-licensed weights. This marks it as the first non-OpenAI/Google model 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 market leaders, DeepSeek has published a detailed training methodology in their paper.
The design is also incredibly economical, 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 common knowledge was that much better models needed more information and compute. While that's still legitimate, models like o1 and R1 demonstrate an option: inference-time scaling through reasoning.

The Essentials

The DeepSeek-R1 paper provided multiple 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 utilizes 2 major concepts:

1. A multi-stage pipeline where a little set of cold-start data kickstarts the model, followed by massive RL.
2. Group Relative Policy Optimization (GRPO), a reinforcement learning method that relies on comparing multiple design outputs per timely to avoid the requirement for a separate critic.

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

R1-Zero vs R1

R1-Zero applies Reinforcement Learning (RL) straight to DeepSeek-V3-Base with no monitored fine-tuning (SFT). RL is used to optimize the model's policy to optimize benefit.
R1-Zero attains exceptional accuracy but sometimes produces confusing outputs, such as mixing numerous languages in a single action. R1 repairs that by integrating restricted supervised fine-tuning and numerous RL passes, which enhances both accuracy and readability.

It is intriguing how some languages may reveal certain concepts much better, which leads the design to pick the most expressive language for the job.

Training Pipeline

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

It's interesting that their training pipeline varies from the typical:

The normal training method: Pretraining on large dataset (train to anticipate next word) to get the base design → supervised fine-tuning → preference tuning via 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 guarantee the RL process has a decent starting point. This provides a good model to begin RL.
First RL Stage: Apply GRPO with rule-based rewards to improve thinking correctness and format (such as requiring chain-of-thought into thinking 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 model but with weak basic capabilities, e.g., bad formatting and language mixing.
Rejection Sampling + general information: Create brand-new SFT information through rejection sampling on the RL checkpoint (from action 2), integrated with supervised 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 reasoning + 200k general jobs) for wider abilities. This step resulted in a strong reasoning design with basic abilities.
Second RL Stage: Add more reward signals (helpfulness, harmlessness) to improve the final model, in addition to the thinking rewards. The outcome 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 strategy where you utilize an instructor design to improve a trainee design by producing training data for the trainee design.
The instructor is typically a bigger model than the trainee.

Group Relative Policy Optimization (GRPO)

The basic concept behind utilizing reinforcement knowing for LLMs is to fine-tune the design's policy so that it naturally produces more accurate and helpful responses.
They used a reward system that inspects not only for accuracy but also for proper format and language consistency, so the design slowly learns to prefer responses that satisfy these quality criteria.

In this paper, they motivate the R1 model to create chain-of-thought thinking through RL training with GRPO.
Instead of adding a different module at inference time, the training process itself nudges the model to produce detailed, detailed outputs-making the chain-of-thought an emerging habits of the enhanced policy.

What makes their approach particularly interesting is its reliance on straightforward, rule-based reward functions.
Instead of depending on pricey external models or human-graded examples as in conventional RLHF, the RL utilized for R1 utilizes easy criteria: it might provide a higher benefit if the answer is right, if it follows the anticipated/ format, and if the language of the answer matches that of the timely.
Not depending on a benefit design likewise implies you do not need 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 timely, the model creates different reactions.
2. Each action receives a scalar benefit based on aspects like precision, formatting, and language consistency.
3. Rewards are changed relative to the group's efficiency, basically determining just how much better each action is compared to the others.
4. The model updates its strategy a little to favor reactions with higher relative advantages. It just makes minor adjustments-using methods like clipping and a KL penalty-to ensure the policy does not stray too far from its initial behavior.

A cool element of GRPO is its versatility. You can utilize simple rule-based benefit functions-for circumstances, awarding a bonus when the model correctly utilizes the syntax-to guide the training.

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

For those aiming to dive deeper, Will Brown has actually composed rather a good application of training an LLM with RL utilizing GRPO. GRPO has actually also already been contributed to the Transformer Reinforcement Learning (TRL) library, which is another good 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 methodologies they have actually presented in their paper, demo.qkseo.in I want to highlight a passage from the DeepSeekMath paper, based upon a point Yannic Kilcher made in his video.

These findings suggest that RL improves the model's overall efficiency by rendering the output distribution more robust, simply put, it seems that the enhancement is credited to enhancing the appropriate response from TopK rather than the enhancement of fundamental abilities.

To put it simply, RL fine-tuning tends to shape the output distribution so that the highest-probability outputs are more likely to be appropriate, despite the fact that the overall ability (as measured by the variety of proper answers) is mainly present in the pretrained model.

This suggests that support learning on LLMs is more about refining and "forming" the existing distribution of actions rather than enhancing the model with entirely brand-new capabilities.
Consequently, while RL techniques such as PPO and GRPO can produce considerable performance gains, there appears to be an intrinsic ceiling identified by the underlying model'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 excited to see how it unfolds!

Running DeepSeek-R1

I've utilized DeepSeek-R1 through the main chat interface for numerous issues, which it seems to resolve all right. The additional search performance makes it even better to utilize.

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

I also 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 perform when deployed on a single H100 GPU-not to extensively test the design's capabilities.

671B via 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 running on the GPU), running by means of llama.cpp:

29 layers appeared to be the sweet area given this configuration.

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 video gaming setup.
Digital Spaceport composed a complete 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 quite bearable for any severe work, however it's enjoyable to run these big models on available hardware.

What matters most to me is a mix of effectiveness and time-to-usefulness in these designs. Since thinking models require to believe before addressing, their time-to-usefulness is typically higher than other models, however their effectiveness is also generally higher.
We require to both maximize effectiveness and lessen time-to-usefulness.

70B by means of Ollama

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

GPU utilization 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 dish to replicate 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 a novel 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 presents DeepSeek-R1, an open-source thinking model that matches the performance of OpenAI's o1. It provides a detailed method for training such designs using large-scale reinforcement knowing methods.
DeepSeek-V3 Technical Report (December 2024) This report talks about the implementation of an FP8 blended accuracy training framework verified on a very large-scale design, attaining both sped up training and lowered GPU memory usage.
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism (January 2024) This paper digs into scaling laws and provides findings that facilitate the scaling of large-scale models in open-source configurations. It presents the DeepSeek LLM task, dedicated to advancing open-source language designs with a long-lasting 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 models 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 identified by economical training and effective inference.
DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence (June 2024) This research study introduces DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that attains efficiency comparable to GPT-4 Turbo in code-specific tasks.

Interesting occasions

- Hong Kong University reproduces R1 results (Jan 25, users.atw.hu '25).
- Huggingface reveals huggingface/open-r 1: Fully open recreation of DeepSeek-R1 to replicate R1, totally open source (Jan 25, '25).
- OpenAI researcher verifies the DeepSeek group individually found and used some core ideas the OpenAI team utilized on the way to o1

Liked this post? Join the newsletter.