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 surpass-OpenAI's o1 design in many standards, however it likewise includes fully MIT-licensed weights. This marks it as the first non-OpenAI/Google design to deliver strong reasoning abilities in an open and available way.

What makes DeepSeek-R1 particularly interesting is its openness. Unlike the less-open methods from some industry leaders, DeepSeek has actually released a detailed training method in their paper.
The design is also extremely cost-effective, 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 better models needed more data and compute. While that's still legitimate, models like o1 and R1 demonstrate an alternative: inference-time scaling through reasoning.

The Essentials

The DeepSeek-R1 paper presented several models, but main amongst them were R1 and R1-Zero. Following these are a series of distilled models that, while fascinating, I will not talk about here.

DeepSeek-R1 utilizes two significant ideas:

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

R1 and R1-Zero are both thinking models. This basically implies they do Chain-of-Thought before answering. For the R1 series of designs, this takes form as thinking within a tag, before addressing 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 model's policy to maximize reward.
R1-Zero attains excellent accuracy but in some cases produces complicated outputs, such as blending several languages in a single response. R1 repairs that by integrating restricted supervised fine-tuning and several RL passes, which enhances both correctness and readability.

It is fascinating how some languages may reveal certain concepts better, which leads the model to choose the most expressive language for the task.

Training Pipeline

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

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

The typical training strategy: Pretraining on big dataset (train to predict next word) to get the base model → monitored fine-tuning → choice tuning through 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 couple of thousand Chain-of-Thought (CoT) samples to ensure the RL procedure has a good starting point. This provides an excellent model to start RL.
First RL Stage: Apply GRPO with rule-based benefits to improve reasoning correctness and formatting (such as requiring chain-of-thought into believing tags). When they were near merging in the RL process, they transferred to the next step. The result of this step is a strong reasoning model but with weak basic abilities, e.g., bad formatting and language blending.
Rejection Sampling + basic data: Create brand-new SFT information through rejection tasting on the RL checkpoint (from action 2), combined with monitored information 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 basic jobs) for more comprehensive capabilities. This step led to a strong thinking model with general capabilities.
Second RL Stage: Add more benefit signals (helpfulness, harmlessness) to fine-tune the final model, in addition to the reasoning rewards. The result is DeepSeek-R1.
They also did design distillation for a number of Qwen and Llama designs on the reasoning traces to get distilled-R1 models.

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

Group Relative Policy Optimization (GRPO)

The standard concept behind using reinforcement knowing for LLMs is to tweak the model's policy so that it naturally produces more precise and beneficial answers.
They used a benefit system that examines not only for accuracy however likewise for proper formatting and language consistency, so the model slowly discovers to prefer reactions that meet these quality criteria.

In this paper, they encourage the R1 design to generate chain-of-thought thinking through RL training with GRPO.
Rather than including a separate module at reasoning time, the training procedure itself nudges the design to produce detailed, detailed outputs-making the chain-of-thought an emergent habits of the enhanced policy.

What makes their technique particularly intriguing is its reliance on straightforward, rule-based reward functions.
Instead of depending on costly external designs or human-graded examples as in conventional RLHF, the RL utilized for R1 utilizes simple criteria: it might give a greater reward if the answer is right, if it follows the anticipated/ formatting, and if the language of the response matches that of the timely.
Not counting on a reward model likewise indicates you don't have to hang out and effort training it, and it does not take memory and compute far 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 responses.
2. Each action gets a scalar reward based upon elements like accuracy, formatting, and language consistency.
3. Rewards are adjusted relative to the group's performance, basically measuring how much better each action is compared to the others.
4. The design updates its strategy somewhat to prefer responses with greater relative benefits. It only makes minor adjustments-using strategies 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 utilize easy rule-based reward functions-for circumstances, granting a perk when the design correctly uses the syntax-to guide the training.

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

For those aiming to dive much deeper, Will Brown has written rather a great application of training an LLM with RL utilizing GRPO. GRPO has also already been contributed to the Transformer Reinforcement Learning (TRL) library, which is another great resource.
Finally, Yannic Kilcher has a fantastic 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 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 boosts the model's total performance by rendering the output circulation more robust, to put it simply, it appears that the enhancement is attributed to boosting the proper action from TopK rather than the enhancement of essential abilities.

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

This suggests that reinforcement knowing on LLMs is more about refining and "forming" the existing distribution of reactions instead of enhancing the design with completely brand-new capabilities.
Consequently, while RL techniques such as PPO and GRPO can produce substantial performance gains, there appears to be 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 thrilled to see how it unfolds!

Running DeepSeek-R1

I have actually used DeepSeek-R1 by means of the main chat user interface for numerous problems, which it appears to fix well enough. The additional search functionality makes it even better to use.

Interestingly, o3-mini(-high) was released as I was composing this post. From my preliminary testing, R1 seems more powerful at math than o3-mini.

I likewise 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 goal was to see how the design would perform when released on a single H100 GPU-not to thoroughly 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 through llama.cpp:

29 layers appeared to be the sweet spot offered this configuration.

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 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, the tokens/s isn't quite manageable for qoocle.com any severe work, but it's fun 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 need to think before responding to, their time-to-usefulness is typically higher than other designs, however their usefulness is also normally greater.
We require to both maximize effectiveness and decrease time-to-usefulness.

70B through Ollama

70.6 b params, 4-bit KM quantized DeepSeek-R1 running through 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 via 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 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 an unique autoregressive structure that combines multimodal understanding and generation. It can both comprehend and produce images.
DeepSeek-R1: Incentivizing Reasoning Capability in Large Language Models through Reinforcement Learning (January 2025) This paper presents DeepSeek-R1, an open-source thinking model that matches the efficiency of OpenAI's o1. It presents a detailed methodology for training such designs using massive support learning techniques.
DeepSeek-V3 Technical Report (December 2024) This report goes over the application of an FP8 blended accuracy training framework validated on an extremely massive model, attaining both accelerated training and decreased 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, dedicated to advancing open-source language designs with a long-lasting perspective.
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 models trained from scratch on 2 trillion tokens. The designs are pre-trained on a high-quality project-level code corpus and utilize a fill-in-the-blank job to enhance 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 characterized by economical training and efficient 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 performance similar to GPT-4 Turbo in code-specific jobs.

Interesting occasions

- Hong Kong University replicates R1 outcomes (Jan 25, '25).
- Huggingface reveals huggingface/open-r 1: Fully open reproduction of DeepSeek-R1 to reproduce R1, totally open source (Jan 25, '25).
- OpenAI scientist verifies the DeepSeek team independently discovered and used some core ideas the OpenAI team utilized on the way to o1

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