Understanding DeepSeek R1 - roy

DeepSeek-R1 is an open-source language model constructed on DeepSeek-V3-Base that's been making waves in the AI neighborhood. Not just does it match-or even surpass-OpenAI's o1 model in many standards, but it likewise comes with totally MIT-licensed weights. This marks it as the very first non-OpenAI/Google design to provide strong thinking abilities in an open and available way.

What makes DeepSeek-R1 especially interesting is its openness. Unlike the less-open methods from some industry leaders, DeepSeek has published a detailed training approach in their paper. The design is likewise extremely affordable, 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 wisdom was that much better designs needed more information and compute. While that's still valid, designs like o1 and R1 show an option: inference-time scaling through reasoning.

The Essentials

The DeepSeek-R1 paper provided several models, however main amongst them were R1 and R1-Zero. Following these are a series of distilled models that, while fascinating, I won't discuss here.

DeepSeek-R1 utilizes two 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 support learning technique that depends on comparing multiple model outputs per timely to avoid the requirement for a different critic.

R1 and R1-Zero are both reasoning designs. This essentially suggests they do Chain-of-Thought before answering. For the R1 series of designs, this takes type as thinking within a tag, before responding to with a final summary.

R1-Zero vs R1

R1-Zero uses Reinforcement Learning (RL) straight to DeepSeek-V3-Base without any supervised fine-tuning (SFT). RL is used to enhance the model's policy to maximize benefit. R1-Zero attains outstanding accuracy but in some cases produces complicated outputs, such as blending multiple languages in a single action. R1 repairs that by incorporating limited supervised fine-tuning and multiple RL passes, which improves both correctness and readability.

It is interesting how some languages might reveal certain concepts better, which leads the model to pick the most meaningful language for the task.

Training Pipeline

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

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

The typical training method: Pretraining on big dataset (train to anticipate next word) to get the base design → monitored 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 good starting point. This offers a great design to begin RL. First RL Stage: Apply GRPO with rule-based rewards to improve thinking accuracy and wiki.project1999.com formatting (such as forcing chain-of-thought into thinking tags). When they were near convergence in the RL procedure, they moved to the next action. The result of this action is a strong thinking design but with weak general capabilities, e.g., poor format and language mixing. Rejection Sampling + general data: Create brand-new SFT data through rejection sampling on the RL checkpoint (from step 2), integrated with supervised information 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 thinking + 200k basic jobs) for more comprehensive capabilities. This step led to a strong reasoning design with general abilities. Second RL Stage: Add more reward signals (helpfulness, harmlessness) to fine-tune the final model, in addition to the thinking benefits. The outcome is DeepSeek-R1. They likewise did model distillation for several Qwen and Llama designs on the reasoning traces to get distilled-R1 models.

Model distillation is a strategy where you utilize a teacher design to improve a trainee design by creating training data for the trainee design. The instructor is generally a larger design than the trainee.

Group Relative Policy Optimization (GRPO)

The basic idea behind using support knowing for LLMs is to fine-tune the design's policy so that it naturally produces more precise and beneficial answers. They utilized a benefit system that examines not only for correctness but also for correct format and language consistency, so the design slowly learns to favor responses that fulfill these quality requirements.

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

What makes their approach particularly fascinating is its dependence on straightforward, rule-based benefit . Instead of depending upon expensive external designs or human-graded examples as in traditional RLHF, the RL used for R1 uses basic requirements: it might offer a higher reward if the answer is proper, if it follows the expected/ formatting, and if the language of the response matches that of the timely. Not depending on a reward design also indicates you do not have to invest time and effort training it, and it does not take memory and calculate away from your main model.

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

1. For each input timely, the model generates various responses. 2. Each response receives a scalar reward based upon factors like accuracy, format, and language consistency. 3. Rewards are adjusted relative to the group's efficiency, essentially measuring how much better each response is compared to the others. 4. The design updates its method slightly to prefer responses with higher relative benefits. It only makes slight adjustments-using techniques like clipping and a KL penalty-to make sure the policy doesn't stray too far from its original habits.

A cool element of GRPO is its versatility. You can utilize basic rule-based reward functions-for instance, awarding a bonus when the design correctly utilizes the syntax-to guide the training.

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

For those aiming to dive much deeper, Will Brown has written rather a good application of training an LLM with RL utilizing GRPO. GRPO has also currently been added to the Transformer Reinforcement Learning (TRL) library, which is another great 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 methods they've provided in their paper, I desire to highlight a passage from the DeepSeekMath paper, based upon a point Yannic Kilcher made in his video.

These findings indicate that RL improves the model's overall efficiency by rendering the output distribution more robust, simply put, it appears that the enhancement is associated to enhancing the proper action from TopK instead of the improvement of fundamental abilities.

To put it simply, RL fine-tuning tends to form the output circulation so that the highest-probability outputs are most likely to be proper, although the total capability (as measured by the variety of correct responses) is mainly present in the pretrained model.

This recommends that reinforcement learning on LLMs is more about refining and "forming" the existing circulation of responses rather than enhancing the model with entirely new capabilities. Consequently, while RL strategies such as PPO and GRPO can produce significant efficiency gains, there seems a fundamental 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 thrilled to see how it unfolds!

Running DeepSeek-R1

I have actually used DeepSeek-R1 via the main chat user interface for various issues, which it seems to resolve all right. The extra search functionality makes it even better to use.

Interestingly, o3-mini(-high) was launched as I was writing this post. From my preliminary testing, R1 seems more powerful at math 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 model would perform when deployed on a single H100 GPU-not to thoroughly test the design's capabilities.

671B through 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 seemed to be the sweet area offered this configuration.

Performance:

A r/localllama user explained that they had the ability to overcome 2 tok/sec with DeepSeek R1 671B, without utilizing their GPU on their regional video gaming setup. Digital Spaceport composed a full guide on how to run Deepseek R1 671b completely in your area 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 any severe work, however 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 designs. Since reasoning models require to believe before addressing, their time-to-usefulness is usually higher than other designs, but their effectiveness is also normally greater. We require to both optimize usefulness and decrease time-to-usefulness.

70B through Ollama

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

GPU utilization 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 via Reinforcement Learning [2402.03300] DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models DeepSeek R1 - Notion (Building a totally local "deep scientist" with DeepSeek-R1 - YouTube). DeepSeek R1's dish 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 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 an unique autoregressive framework that unifies multimodal understanding and generation. It can both comprehend and create 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 reasoning model that equals the performance of OpenAI's o1. It presents a detailed method for training such designs using large-scale reinforcement learning methods. DeepSeek-V3 Technical Report (December 2024) This report goes over the application of an FP8 mixed precision training structure validated on a very large-scale model, attaining both sped up training and lowered GPU memory usage. DeepSeek LLM: Scaling Open-Source Language Models with Longtermism (January 2024) This paper delves into scaling laws and provides findings that help with the scaling of large-scale models in open-source configurations. It introduces the DeepSeek LLM task, committed to advancing open-source language designs with a long-term point of view. 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 series of open-source code models trained from scratch on 2 trillion tokens. The designs are pre-trained on a premium project-level code corpus and employ a fill-in-the-blank task 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 cost-effective training and efficient reasoning. DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence (June 2024) This research presents DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language design that attains performance comparable to GPT-4 Turbo in code-specific jobs.

Interesting events

- Hong Kong University replicates R1 outcomes (Jan 25, '25). - Huggingface reveals huggingface/open-r 1: Fully open reproduction of DeepSeek-R1 to replicate R1, fully open source (Jan 25, '25). - OpenAI researcher confirms the DeepSeek team independently found and used some core ideas the OpenAI team used on the method to o1

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