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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 design in lots of criteria, but it also comes with 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 manner.

What makes DeepSeek-R1 especially interesting is its transparency. Unlike the less-open methods from some industry leaders, DeepSeek has actually published a detailed training methodology in their paper.
The model is likewise remarkably cost-effective, 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 common wisdom was that better designs needed more information and calculate. While that’s still legitimate, designs like o1 and R1 show an alternative: inference-time scaling through reasoning.

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 designs that, while intriguing, I won’t discuss here.

DeepSeek-R1 uses 2 major ideas:

1. A multi-stage pipeline where a little set of cold-start information kickstarts the model, followed by massive RL.
2. Group Relative Policy Optimization (GRPO), a support learning method that depends on comparing several design outputs per prompt to prevent the need for a different critic.

R1 and R1-Zero are both thinking models. This essentially suggests they do Chain-of-Thought before responding to. For the R1 series of models, this takes kind as believing within a tag, before responding to with a .

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 optimize reward.
R1-Zero attains outstanding precision but sometimes produces complicated outputs, such as mixing numerous languages in a single action. R1 repairs that by incorporating limited supervised fine-tuning and numerous RL passes, which improves both accuracy and readability.

It is fascinating how some languages might express certain ideas better, which leads the model to choose the most meaningful language for the job.

Training Pipeline

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

It’s intriguing that their training pipeline differs from the normal:

The typical training technique: Pretraining on big dataset (train to predict next word) to get the base model → monitored fine-tuning → preference tuning through RLHF
R1-Zero: Pretrained → RL
R1: Pretrained → Multistage training pipeline with multiple 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 process has a good starting point. This offers a good model to begin RL.
First RL Stage: Apply GRPO with rule-based benefits to improve thinking accuracy and formatting (such as forcing chain-of-thought into believing tags). When they were near merging in the RL procedure, they transferred to the next step. The result of this action is a strong thinking design however with weak general abilities, e.g., bad formatting and language mixing.
Rejection Sampling + basic information: Create new SFT data through rejection tasting on the RL checkpoint (from action 2), combined with supervised information from the DeepSeek-V3-Base design. They collected around 600k top quality reasoning samples.
Second Fine-Tuning: Fine-tune DeepSeek-V3-Base again on 800k overall samples (600k reasoning + 200k basic jobs) for wider abilities. This step resulted in a strong reasoning design with basic abilities.
Second RL Stage: Add more benefit signals (helpfulness, harmlessness) to refine the final design, in addition to the reasoning rewards. The result is DeepSeek-R1.
They also did model distillation for several Qwen and Llama designs on the reasoning traces to get distilled-R1 designs.

Model distillation is a method where you utilize an instructor model to enhance a trainee design by generating training data for the trainee design.
The instructor is typically a bigger model than the trainee.

Group Relative Policy Optimization (GRPO)

The fundamental idea behind utilizing support knowing for LLMs is to tweak the design’s policy so that it naturally produces more precise and beneficial answers.
They used a benefit system that inspects not just for correctness however likewise for correct formatting and language consistency, so the design gradually learns to prefer reactions that meet these quality criteria.

In this paper, they encourage the R1 model to generate chain-of-thought reasoning through RL training with GRPO.
Rather than including 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 approach especially intriguing is its dependence on straightforward, rule-based reward functions.
Instead of depending on expensive external models or human-graded examples as in traditional RLHF, the RL used for R1 utilizes easy requirements: it might provide a higher reward if the answer is appropriate, if it follows the expected/ format, and if the language of the answer matches that of the prompt.
Not relying on a benefit model likewise means you do not need to hang around and effort training it, and it does not 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 design creates different responses.
2. Each reaction receives a scalar benefit based upon elements like precision, formatting, and language consistency.
3. Rewards are adjusted relative to the group’s performance, basically determining how much better each reaction is compared to the others.
4. The design updates its method somewhat to favor reactions with greater relative benefits. It only makes slight adjustments-using strategies like clipping and a KL penalty-to ensure the policy doesn’t stray too far from its initial behavior.

A cool aspect of GRPO is its flexibility. You can use basic rule-based benefit functions-for instance, granting a reward when the model properly utilizes the syntax-to guide the training.

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

For those aiming to dive much deeper, Will Brown has actually composed quite a great execution of training an LLM with RL utilizing GRPO. GRPO has also currently been contributed to the Transformer Reinforcement Learning (TRL) library, which is another good resource.
Finally, Yannic Kilcher has a great 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 want to highlight a passage from the DeepSeekMath paper, based upon a point Yannic Kilcher made in his video.

These findings show that RL improves the design’s total performance by rendering the output circulation more robust, in other words, it appears that the improvement is credited to enhancing the right response from TopK instead of the enhancement of fundamental capabilities.

In other words, RL fine-tuning tends to shape the output distribution so that the highest-probability outputs are most likely to be correct, although the overall capability (as measured by the variety of correct answers) is mainly present in the pretrained model.

This recommends that support knowing on LLMs is more about refining and « forming » the existing distribution of actions instead of enhancing the model with completely new abilities.
Consequently, while RL techniques such as PPO and GRPO can produce considerable performance gains, there seems a fundamental ceiling figured out 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 huge turning point. I’m excited to see how it unfolds!

Running DeepSeek-R1

I’ve used DeepSeek-R1 through the main chat interface for numerous issues, which it appears to fix all right. The extra search functionality makes it even better to utilize.

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

I likewise leased a single H100 by means of Lambda Labs for suvenir51.ru $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 thoroughly test the design’s abilities.

671B through 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 area offered this configuration.

Performance:

A r/localllama user explained that they were able to get over 2 tok/sec with DeepSeek R1 671B, without utilizing their GPU on their local video gaming setup.
Digital Spaceport wrote a complete guide on how to run Deepseek R1 671b completely 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 rather bearable for any severe work, however it’s fun to run these large models on available hardware.

What matters most to me is a mix of usefulness and time-to-usefulness in these models. Since thinking models require to believe before addressing, their time-to-usefulness is generally greater than other designs, however their effectiveness is likewise generally greater.
We need to both take full advantage of effectiveness and lessen time-to-usefulness.

70B by means of Ollama

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

GPU usage shoots up 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 totally regional « deep researcher » with DeepSeek-R1 – YouTube).
DeepSeek R1‘s recipe to duplicate 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 structure that combines multimodal understanding and generation. It can both understand and generate images.
DeepSeek-R1: Incentivizing Reasoning Capability in Large Language Models by means of Reinforcement Learning (January 2025) This paper introduces DeepSeek-R1, an open-source thinking design that measures up to the performance of OpenAI’s o1. It presents a detailed approach for training such models using large-scale support knowing techniques.
DeepSeek-V3 Technical Report (December 2024) This report talks about the application of an FP8 mixed accuracy training structure verified on an extremely large-scale design, attaining both accelerated training and minimized GPU memory use.
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism (January 2024) This paper dives into scaling laws and provides findings that assist in the scaling of large-scale models in open-source configurations. It introduces the DeepSeek LLM project, dedicated 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 introduces the DeepSeek-Coder series, a variety of open-source code designs trained from scratch on 2 trillion tokens. The designs 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 model characterized by cost-effective training and efficient 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 performance similar to GPT-4 Turbo in code-specific jobs.

Interesting events

– Hong Kong University replicates R1 results (Jan 25, ’25).
– Huggingface announces huggingface/open-r 1: Fully open recreation of DeepSeek-R1 to replicate R1, archmageriseswiki.com fully open source (Jan 25, ’25).
– OpenAI scientist validates the DeepSeek team separately discovered and used some core concepts the OpenAI group used on the way to o1

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