Understanding DeepSeek R1

DeepSeek-R1 is an open-source language design developed 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 many standards, but it likewise comes with fully MIT-licensed weights. This marks it as the first non-OpenAI/Google model to deliver strong thinking abilities in an open and available manner.

What makes DeepSeek-R1 especially interesting is its transparency. Unlike the less-open approaches from some market leaders, DeepSeek has published a detailed training method in their paper. The design is likewise incredibly affordable, 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 much better designs needed more information and calculate. While that's still valid, designs like o1 and R1 show an alternative: inference-time scaling through thinking.

The Essentials

The DeepSeek-R1 paper presented 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 go over here.

DeepSeek-R1 utilizes two major ideas:

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

R1 and R1-Zero are both reasoning designs. This essentially suggests they do Chain-of-Thought before responding to. For the R1 series of models, this takes type as thinking 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 utilized to optimize the design's policy to optimize benefit. R1-Zero attains excellent accuracy however often produces complicated outputs, such as mixing multiple languages in a single response. R1 repairs that by including minimal supervised fine-tuning and several RL passes, which improves both accuracy and readability.

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

Training Pipeline

The training pipeline that DeepSeek released in the R1 paper is exceptionally fascinating. It showcases how they created such strong reasoning models, and what you can get out of each phase. This consists of 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 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 → preference tuning by means of 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 make sure the RL procedure has a good starting point. This provides an excellent model to begin RL. First RL Stage: Apply GRPO with rule-based benefits to enhance thinking correctness and format (such as forcing chain-of-thought into thinking tags). When they were near convergence in the RL procedure, they relocated to the next action. The result of this action is a strong reasoning model but with weak basic capabilities, e.g., bad format and language blending. Rejection Sampling + basic data: Create new SFT data through rejection tasting on the RL checkpoint (from step 2), integrated with monitored information from the DeepSeek-V3-Base model. They gathered around 600k high-quality thinking samples. Second Fine-Tuning: Fine-tune DeepSeek-V3-Base again on 800k overall samples (600k reasoning + 200k basic jobs) for wider capabilities. This step led to a strong thinking model with basic capabilities. Second RL Stage: Add more reward signals (helpfulness, harmlessness) to improve the final model, in addition to the reasoning benefits. The result is DeepSeek-R1. They likewise did model distillation for numerous Qwen and Llama models on the reasoning traces to get distilled-R1 designs.

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

Group Relative Policy Optimization (GRPO)

The basic idea behind using reinforcement knowing for LLMs is to tweak the model's policy so that it naturally produces more accurate and helpful responses. They utilized a reward system that examines not just for accuracy but also for proper format and language consistency, so the model slowly discovers to favor reactions that fulfill these quality criteria.

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

What makes their method particularly intriguing is its reliance on straightforward, rule-based benefit functions. Instead of depending upon pricey external models or human-graded examples as in standard RLHF, the RL utilized for R1 utilizes easy requirements: it may provide a greater benefit if the response is proper, if it follows the anticipated/ format, and if the language of the response matches that of the timely. Not counting on a benefit model likewise suggests you don't need to hang around and effort training it, and it does not take memory and calculate away from your main design.

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

1. For each input prompt, the design generates different actions. 2. Each reaction receives a scalar benefit based on aspects like accuracy, format, and language consistency. 3. Rewards are changed relative to the group's performance, essentially measuring just how much better each response is compared to the others. 4. The model updates its method slightly to favor reactions with greater relative advantages. It just makes slight adjustments-using methods like clipping and yewiki.org a KL penalty-to make sure the policy does not stray too far from its original behavior.

A cool aspect of GRPO is its flexibility. You can use basic rule-based benefit functions-for instance, awarding a bonus when the design correctly uses the syntax-to guide the training.

While DeepSeek utilized GRPO, you could utilize alternative methods rather (PPO or PRIME).

For those aiming to dive deeper, Will Brown has actually composed rather a good execution of training an LLM with RL utilizing GRPO. GRPO has also already been contributed to the Transformer Reinforcement Learning (TRL) library, which is another good 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 approaches they have actually presented in their paper, I wish to highlight a passage from the DeepSeekMath paper, based on a point Yannic Kilcher made in his video.

These findings indicate that RL boosts the model's overall performance by rendering the output distribution more robust, simply put, it seems that the improvement is associated to increasing the appropriate reaction from TopK rather than the improvement of fundamental capabilities.

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

This suggests that support learning on LLMs is more about refining and "forming" the existing distribution of reactions instead of enhancing the design with entirely new abilities. Consequently, while RL methods such as PPO and GRPO can produce substantial performance gains, there seems an intrinsic 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 big turning point. I'm excited to see how it unfolds!

Running DeepSeek-R1

I've used DeepSeek-R1 by means of the main chat interface for different problems, which it seems to solve well enough. The additional search functionality makes it even nicer to use.

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

I also rented 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 design would perform when deployed on a single H100 GPU-not to extensively test the design's abilities.

671B via 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 appeared to be the sweet spot offered this setup.

Performance:

A r/localllama user explained that they were able to overcome 2 tok/sec with DeepSeek R1 671B, without using their GPU on their local video gaming setup. Digital Spaceport composed a full guide on how to run Deepseek R1 671b totally 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 any severe work, but it's enjoyable 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 reasoning designs need to think before addressing, their time-to-usefulness is typically higher than other designs, however their effectiveness is likewise usually greater. We need to both take full advantage of usefulness and decrease time-to-usefulness.

70B via 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 by means of Reinforcement Learning [2402.03300] DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models DeepSeek R1 - Notion (Building a completely local "deep researcher" 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 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 structure that combines multimodal understanding and generation. It can both understand and produce images. DeepSeek-R1: Incentivizing Reasoning Capability in Large Language Models via Reinforcement Learning (January 2025) This paper presents DeepSeek-R1, an open-source thinking design that rivals the efficiency of OpenAI's o1. It provides a detailed approach for training such models utilizing large-scale reinforcement knowing techniques. DeepSeek-V3 Technical Report (December 2024) This report talks about the execution of an FP8 blended accuracy training framework confirmed on an incredibly massive model, attaining both accelerated training and reduced GPU memory use. DeepSeek LLM: Scaling Open-Source Language Models with Longtermism (January 2024) This paper explores scaling laws and presents findings that help with the scaling of massive models in open-source configurations. It introduces the DeepSeek LLM project, dedicated to advancing open-source models 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 range 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 wiki.snooze-hotelsoftware.de use a fill-in-the-blank job 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 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 introduces DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language design that attains efficiency comparable to GPT-4 Turbo in code-specific tasks.

Interesting occasions

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

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