DeepSeek-R1 is an open-source language model developed on DeepSeek-V3-Base that's been making waves in the AI neighborhood. Not only does it match-or even surpass-OpenAI's o1 model in many criteria, however it also features fully MIT-licensed weights. This marks it as the first non-OpenAI/Google model 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 market leaders, DeepSeek has released a detailed training method in their paper.
The design is also remarkably cost-efficient, 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 required more information and calculate. While that's still valid, models like o1 and R1 show an alternative: inference-time scaling through reasoning.

The Essentials

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

DeepSeek-R1 utilizes 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 approach that relies on comparing numerous model outputs per prompt to prevent the need for a separate critic.

R1 and R1-Zero are both reasoning models. This basically suggests 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 enhance the design's policy to make the most of benefit.
R1-Zero attains excellent precision but often produces confusing outputs, such as mixing several languages in a single response. R1 repairs that by incorporating limited supervised fine-tuning and multiple RL passes, which improves both accuracy and readability.

It is interesting how some languages might express certain concepts better, which leads the model to select the most expressive language for the job.

Training Pipeline

The training pipeline that DeepSeek published in the R1 paper is immensely interesting. It showcases how they created such strong reasoning designs, and what you can expect from each stage. This includes the problems that the resulting models from each stage have, and how they solved it in the next phase.

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

The normal training technique: Pretraining on large dataset (train to predict next word) to get the base model → supervised 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 few thousand Chain-of-Thought (CoT) samples to ensure the RL procedure has a decent beginning point. This provides a good model to begin RL.
First RL Stage: Apply GRPO with rule-based benefits to improve reasoning correctness and formatting (such as requiring chain-of-thought into thinking tags). When they were near convergence in the RL procedure, they transferred to the next step. The result of this step is a strong reasoning model but with weak general capabilities, e.g., poor formatting and language mixing.
Rejection Sampling + general data: Create brand-new SFT data through rejection tasting on the RL checkpoint (from action 2), integrated with monitored information from the DeepSeek-V3-Base model. 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 more comprehensive capabilities. This action resulted in a strong thinking design with basic abilities.
Second RL Stage: Add more benefit signals (helpfulness, harmlessness) to fine-tune the final model, in addition to the thinking benefits. The result is DeepSeek-R1.
They likewise did model distillation for a number of Qwen and Llama designs on the thinking traces to get distilled-R1 designs.

Model distillation is a method where you utilize an instructor model to improve a trainee design by producing training information for the trainee model.
The teacher is generally a larger model than the trainee.

Group Relative Policy Optimization (GRPO)

The standard concept behind utilizing support learning for LLMs is to tweak the model's policy so that it naturally produces more precise and beneficial answers.
They used a reward system that checks not only for accuracy but also for proper formatting and language consistency, so the model slowly learns to prefer reactions that meet these quality criteria.

In this paper, they encourage the R1 design to produce chain-of-thought thinking through RL training with GRPO.
Rather than including a different module at inference time, the training process itself pushes the design to produce detailed, detailed outputs-making the chain-of-thought an emergent habits of the optimized policy.

What makes their method especially interesting is its dependence on straightforward, rule-based reward functions.
Instead of depending upon pricey external models or human-graded examples as in traditional RLHF, the RL utilized for R1 utilizes basic requirements: it may offer a higher reward if the answer is right, if it follows the anticipated/ formatting, and if the language of the answer matches that of the timely.
Not relying on a benefit design also suggests you don't need to spend time and effort training it, and it doesn't take memory and compute away from your main model.

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

1. For each input timely, the design generates different responses.
2. Each action receives a scalar benefit based upon factors like precision, format, and language consistency.
3. Rewards are changed relative to the group's efficiency, essentially determining how much better each reaction is compared to the others.
4. The model updates its method slightly to favor responses with higher relative advantages. It just makes minor adjustments-using techniques like clipping and a KL penalty-to make sure the policy doesn't stray too far from its initial behavior.

A cool aspect of GRPO is its versatility. You can utilize easy rule-based reward functions-for circumstances, granting a reward when the model properly uses the syntax-to guide the training.

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

For those aiming to dive deeper, Will Brown has actually composed quite a good execution of training an LLM with RL utilizing GRPO. GRPO has likewise currently 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 last note on explaining DeepSeek-R1 and the methods they have actually provided in their paper, I want to highlight a passage from the DeepSeekMath paper, based upon a point Yannic Kilcher made in his video.

These findings show that RL boosts the model's total efficiency by rendering the output distribution more robust, simply put, it seems that the improvement is credited to improving the right reaction from TopK instead of the enhancement of essential capabilities.

To put it simply, RL fine-tuning tends to form the output distribution so that the highest-probability outputs are more most likely to be right, despite the fact that the general capability (as determined by the diversity of correct 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 reactions instead of enhancing the model with completely new abilities.
Consequently, while RL methods such as PPO and GRPO can produce substantial efficiency gains, there seems an intrinsic ceiling identified 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 milestone. I'm delighted to see how it unfolds!

Running DeepSeek-R1

I have actually used DeepSeek-R1 by means of the main chat user interface for numerous issues, which it seems to resolve well enough. The extra search performance makes it even better to use.

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

I likewise leased a single H100 by means of 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 model would carry out when released on a single H100 GPU-not to extensively evaluate the design's capabilities.

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 operating on the GPU), running via llama.cpp:

29 layers seemed 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 gaming setup.
Digital Spaceport wrote a full 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, asteroidsathome.net the tokens/s isn't rather bearable for any major work, however it's fun to run these large designs on available hardware.

What matters most to me is a combination of effectiveness and time-to-usefulness in these designs. Since thinking designs require to think before answering, their time-to-usefulness is usually higher than other models, but their usefulness is likewise normally higher.
We need to both make the most of usefulness and decrease time-to-usefulness.

70B through Ollama

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

GPU utilization 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 completely local "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 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 framework that merges multimodal understanding and generation. It can both understand and generate images.
DeepSeek-R1: Incentivizing Reasoning Capability in Large Language Models through Reinforcement Learning (January 2025) This paper introduces DeepSeek-R1, an open-source thinking model that measures up to the efficiency of OpenAI's o1. It presents a detailed approach for training such designs utilizing massive reinforcement learning methods.
DeepSeek-V3 Technical Report (December 2024) This report discusses the application of an FP8 combined accuracy training structure verified on a very large-scale model, attaining both accelerated training and lowered GPU memory usage.
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism (January 2024) This paper delves into scaling laws and presents findings that facilitate the scaling of large-scale designs in open-source setups. It introduces the DeepSeek LLM job, devoted 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 presents the DeepSeek-Coder series, a variety of open-source code designs trained from scratch on 2 trillion tokens. The models are pre-trained on a high-quality project-level code corpus and utilize a fill-in-the-blank task to improve 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 identified by cost-effective training and effective reasoning.
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 model that attains efficiency equivalent to GPT-4 Turbo in code-specific tasks.

Interesting events

- Hong Kong University reproduces R1 outcomes (Jan 25, '25).
- Huggingface reveals huggingface/open-r 1: Fully open reproduction of DeepSeek-R1 to reproduce R1, completely open source (Jan 25, '25).
- OpenAI researcher validates the DeepSeek group individually discovered and utilized some core ideas the OpenAI group used on the method to o1

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