GitLab

DeepSeek-R1 is an open-source language model developed on DeepSeek-V3-Base that's been making waves in the AI neighborhood. Not just does it match-or even o1 model in many criteria, however it also includes completely MIT-licensed weights. This marks it as the very first non-OpenAI/Google model to deliver strong thinking capabilities in an open and available way.

What makes DeepSeek-R1 especially exciting is its transparency. Unlike the less-open methods from some industry leaders, DeepSeek has released a detailed training approach in their paper. The design is likewise remarkably economical, 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 designs needed more data and calculate. While that's still valid, models like o1 and R1 demonstrate an alternative: inference-time scaling through reasoning.

The Essentials

The DeepSeek-R1 paper presented multiple models, however main amongst them were R1 and R1-Zero. Following these are a series of distilled designs that, while fascinating, I won't talk about here.

DeepSeek-R1 utilizes two significant concepts:

1. A multi-stage pipeline where a small set of cold-start data kickstarts the model, followed by large-scale RL. 2. Group Relative Policy Optimization (GRPO), a support learning method that relies on comparing several model outputs per timely to avoid the need for a separate critic.

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

R1-Zero vs R1

R1-Zero uses Reinforcement Learning (RL) straight to DeepSeek-V3-Base with no monitored fine-tuning (SFT). RL is used to optimize the design's policy to make the most of reward. R1-Zero attains excellent accuracy however in some cases produces complicated outputs, such as mixing multiple languages in a single reaction. R1 repairs that by incorporating restricted supervised fine-tuning and several RL passes, which enhances both correctness and readability.

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

Training Pipeline

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

It's intriguing that their training pipeline differs from the usual:

The typical training technique: Pretraining on big dataset (train to predict next word) to get the base design → supervised fine-tuning → preference tuning via RLHF R1-Zero: Pretrained → RL R1: Pretrained → Multistage training pipeline with multiple SFT and RL phases

Cold-Start Fine-Tuning: Fine-tune DeepSeek-V3-Base on a few thousand Chain-of-Thought (CoT) samples to ensure the RL procedure has a good beginning point. This provides an excellent model to start RL. First RL Stage: Apply GRPO with rule-based rewards to enhance thinking correctness and format (such as requiring chain-of-thought into thinking tags). When they were near merging in the RL process, they relocated to the next step. The result of this action is a strong thinking model however with weak general capabilities, e.g., bad format and language blending. Rejection Sampling + basic information: Create new SFT information through rejection tasting on the RL checkpoint (from action 2), combined 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 reasoning + 200k basic tasks) for wider capabilities. This action led to a strong reasoning model with general abilities. Second RL Stage: Add more reward signals (helpfulness, harmlessness) to improve the final model, in addition to the reasoning rewards. The result is DeepSeek-R1. They likewise did model distillation for several Qwen and Llama models on the reasoning traces to get distilled-R1 models.

Model distillation is a strategy where you use an instructor design to enhance a trainee design by producing training data for the trainee model. The instructor is generally a larger design than the trainee.

Group Relative Policy Optimization (GRPO)

The basic concept behind utilizing reinforcement learning for LLMs is to fine-tune the design's policy so that it naturally produces more precise and useful responses. They used a benefit system that examines not just for correctness however also for proper formatting and language consistency, so the design slowly finds out to favor responses that meet these quality criteria.

In this paper, they encourage the R1 model to create chain-of-thought reasoning through RL training with GRPO. Rather than adding a different module at reasoning time, the training process itself pushes the model to produce detailed, detailed outputs-making the chain-of-thought an emergent behavior of the optimized policy.

What makes their technique particularly intriguing is its reliance on straightforward, rule-based benefit functions. Instead of depending upon costly external models or human-graded examples as in conventional RLHF, the RL used for R1 utilizes simple criteria: it might provide a greater benefit if the response is appropriate, if it follows the anticipated/ format, and if the language of the answer matches that of the timely. Not depending on a benefit model also suggests you do not need to hang around and effort training it, and it doesn't take memory and calculate far from your main model.

GRPO was introduced in the DeepSeekMath paper. Here's how GRPO works:

1. For each input prompt, the model creates different actions. 2. Each action gets a scalar benefit based on elements like precision, format, and language consistency. 3. Rewards are changed relative to the group's performance, basically measuring how much better each response is compared to the others. 4. The model updates its technique a little to favor responses with greater relative benefits. It just makes slight adjustments-using methods like clipping and a KL penalty-to make sure the policy does not stray too far from its initial habits.

A cool element of GRPO is its versatility. You can use basic rule-based reward functions-for circumstances, granting a reward when the design properly utilizes the syntax-to guide the training.

While DeepSeek utilized GRPO, you could use alternative methods instead (PPO or PRIME).

For those aiming to dive much deeper, Will Brown has actually composed rather a nice execution of training an LLM with RL using GRPO. GRPO has actually likewise already been included 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 last note on explaining DeepSeek-R1 and the approaches they've provided in their paper, I desire to highlight a passage from the DeepSeekMath paper, based on a point Yannic Kilcher made in his video.

These findings suggest that RL improves the model's total performance by rendering the output circulation more robust, elearnportal.science simply put, it seems that the improvement is associated to improving the right reaction from TopK instead of the improvement of basic abilities.

In other words, RL fine-tuning tends to form the output distribution so that the highest-probability outputs are more most likely to be proper, even though the general capability (as determined by the diversity of correct answers) is mainly present in the pretrained design.

This recommends that reinforcement learning on LLMs is more about refining and "shaping" the existing circulation of responses rather than endowing the model with entirely new capabilities. Consequently, while RL methods such as PPO and GRPO can produce significant performance gains, there seems an inherent ceiling figured out 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 huge turning point. I'm thrilled to see how it unfolds!

Running DeepSeek-R1

I've used DeepSeek-R1 through the main chat interface for various problems, which it seems to fix well enough. The additional search performance makes it even better to use.

Interestingly, o3-mini(-high) was launched as I was writing this post. From my initial screening, R1 appears stronger 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 perform when deployed on a single H100 GPU-not to extensively check 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 working on the GPU), running via llama.cpp:

29 layers seemed to be the sweet area given this setup.

Performance:

A r/localllama user explained that they were able to overcome 2 tok/sec with DeepSeek R1 671B, without utilizing their GPU on their local 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 serious work, however it's fun to run these large designs on available hardware.

What matters most to me is a mix of effectiveness and time-to-usefulness in these designs. Since reasoning models require to think before addressing, their time-to-usefulness is generally higher than other models, but their usefulness is likewise typically greater. We require to both make the most of effectiveness and decrease time-to-usefulness.

70B via Ollama

70.6 b params, systemcheck-wiki.de 4-bit KM quantized DeepSeek-R1 running via Ollama:

GPU utilization soars 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 fully regional "deep scientist" with DeepSeek-R1 - YouTube). DeepSeek R1's recipe to replicate 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 grandmother - 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 combines multimodal understanding and generation. It can both understand and generate images. DeepSeek-R1: Incentivizing Reasoning Capability in Large Language Models via Reinforcement Learning (January 2025) This paper introduces DeepSeek-R1, an open-source thinking design that rivals the performance of OpenAI's o1. It provides a detailed approach for training such models utilizing massive reinforcement learning methods. DeepSeek-V3 Technical Report (December 2024) This report goes over the application of an FP8 blended precision training framework confirmed on an incredibly large-scale model, attaining both sped up training and decreased GPU memory usage. DeepSeek LLM: Scaling Open-Source Language Models with Longtermism (January 2024) This paper explores scaling laws and provides findings that facilitate the scaling of massive designs in open-source setups. It introduces the DeepSeek LLM job, dedicated 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 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 top quality project-level code corpus and employ 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 model characterized by affordable training and effective reasoning. 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 jobs.

Interesting occasions

- Hong Kong University reproduces R1 outcomes (Jan 25, '25).

• Huggingface reveals huggingface/open-r 1: Fully open reproduction of DeepSeek-R1 to duplicate R1, completely open source (Jan 25, '25).
• OpenAI scientist validates the DeepSeek group independently discovered and utilized some core ideas the OpenAI group used en route to o1
  
  Liked this post? Join the newsletter.