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

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 method in their paper.
The model is likewise extremely 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 much better models needed more data and calculate. While that's still legitimate, models like o1 and R1 show an alternative: inference-time scaling through reasoning.

The Essentials

The DeepSeek-R1 paper presented several designs, however main amongst them were R1 and R1-Zero. Following these are a series of distilled designs that, while interesting, I will not go over here.

DeepSeek-R1 uses two significant concepts:

1. A multi-stage pipeline where a small set of cold-start data kickstarts the design, followed by large-scale RL.
2. Group Relative Policy Optimization (GRPO), a support learning method 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 means they do Chain-of-Thought before addressing. For the R1 series of models, this takes kind as believing within a tag, before answering with a final summary.

R1-Zero vs R1

R1-Zero applies Reinforcement Learning (RL) straight to DeepSeek-V3-Base without any supervised fine-tuning (SFT). RL is utilized to optimize the design's policy to maximize benefit.
R1-Zero attains exceptional accuracy however sometimes produces complicated outputs, such as blending multiple languages in a single response. R1 repairs that by integrating limited monitored fine-tuning and numerous RL passes, which enhances both correctness and readability.

It is intriguing how some languages might reveal certain ideas better, which leads the model to pick the most expressive language for the task.

Training Pipeline

The training pipeline that DeepSeek released in the R1 paper is immensely fascinating. It showcases how they produced such strong reasoning models, and what you can anticipate from each stage. This consists of the problems that the resulting models from each phase have, and how they fixed it in the next stage.

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

The usual training strategy: Pretraining on large dataset (train to predict next word) to get the base design → supervised fine-tuning → choice tuning via RLHF
R1-Zero: Pretrained → RL
R1: Pretrained → Multistage training pipeline with numerous SFT and RL stages

Cold-Start Fine-Tuning: Fine-tune DeepSeek-V3-Base on a few thousand Chain-of-Thought (CoT) samples to make sure the RL process has a decent beginning point. This provides a great model to start RL.
First RL Stage: Apply GRPO with rule-based rewards to improve reasoning correctness and formatting (such as requiring chain-of-thought into believing tags). When they were near convergence in the RL procedure, they relocated to the next step. The result of this action is a strong thinking model however with weak basic abilities, e.g., poor formatting and language blending.
Rejection Sampling + basic information: Create brand-new SFT data through rejection sampling on the RL checkpoint (from action 2), integrated with supervised information from the DeepSeek-V3-Base design. They gathered around 600k premium reasoning samples.
Second Fine-Tuning: Fine-tune DeepSeek-V3-Base again on 800k overall samples (600k thinking + 200k basic tasks) for wider capabilities. This step led to a strong reasoning design with general capabilities.
Second RL Stage: Add more benefit signals (helpfulness, harmlessness) to fine-tune the final design, in addition to the reasoning rewards. The outcome is DeepSeek-R1.
They likewise did model distillation for numerous Qwen and Llama designs on the reasoning traces to get distilled-R1 designs.

Model distillation is a strategy where you utilize an instructor model to enhance a trainee design by producing training information for the trainee design.
The teacher is normally a bigger model than the trainee.

Group Relative Policy Optimization (GRPO)

The basic idea behind utilizing support learning for LLMs is to tweak the design's policy so that it naturally produces more precise and beneficial responses.
They used a benefit system that examines not only for correctness however also for proper formatting and language consistency, so the design gradually learns to prefer responses that meet these quality requirements.

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

What makes their technique especially intriguing is its dependence on straightforward, rule-based reward functions.
Instead of depending upon costly external designs or human-graded examples as in conventional RLHF, the RL utilized for R1 utilizes easy requirements: it might offer a higher benefit if the response is appropriate, utahsyardsale.com if it follows the anticipated/ formatting, and if the language of the response matches that of the prompt.
Not depending on a reward design likewise means you don't need to hang around and effort training it, and it does not take memory and calculate away from your main model.

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

1. For bio.rogstecnologia.com.br each input timely, the model generates various actions.
2. Each action receives a scalar benefit based upon factors like accuracy, format, and language consistency.
3. Rewards are adjusted relative to the group's performance, essentially determining just how much better each action is compared to the others.
4. The design updates its method slightly to prefer actions with greater relative advantages. It just makes slight adjustments-using techniques like clipping and sitiosecuador.com a KL penalty-to ensure the policy doesn't wander off too far from its initial habits.

A cool element of GRPO is its versatility. You can utilize simple rule-based reward functions-for instance, granting a bonus when the model properly utilizes the syntax-to guide the training.

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

For those aiming to dive much deeper, Will Brown has written rather a good implementation of training an LLM with RL utilizing GRPO. GRPO has actually also currently been contributed to the Transformer Reinforcement Learning (TRL) library, which is another good 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 methodologies they have actually presented 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 model's general performance by rendering the output distribution more robust, to put it simply, it seems that the enhancement is attributed to increasing the correct action from TopK rather than the enhancement of essential capabilities.

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

This suggests that support learning on LLMs is more about refining and "forming" the existing distribution of actions rather than endowing the design with totally brand-new abilities.
Consequently, while RL methods such as PPO and GRPO can produce substantial performance gains, there seems an inherent 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 huge milestone. I'm delighted to see how it unfolds!

Running DeepSeek-R1

I've used DeepSeek-R1 through the main chat user interface for various problems, which it appears to solve well enough. The extra search performance makes it even nicer to utilize.

Interestingly, o3-mini(-high) was released as I was writing this post. From my preliminary screening, R1 appears more powerful at mathematics than o3-mini.

I likewise leased a single H100 via Lambda Labs for $2/h (26 CPU cores, 214.7 GB RAM, surgiteams.com 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 extensively check the model's abilities.

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 operating on the GPU), running through 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 wrote a complete 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 bearable for any serious work, however it's fun to run these large models on available hardware.

What matters most to me is a combination of usefulness and time-to-usefulness in these models. Since thinking designs need to think before responding to, their time-to-usefulness is usually higher than other models, however their usefulness is likewise normally higher.
We need to both optimize effectiveness and minimize 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 completely local "deep researcher" with DeepSeek-R1 - YouTube).
DeepSeek R1's dish 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 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 combines multimodal understanding and generation. It can both understand and produce images.
DeepSeek-R1: Incentivizing Reasoning Capability in Large Language Models by means of Reinforcement Learning (January 2025) This paper presents DeepSeek-R1, an open-source reasoning design that measures up to the performance of OpenAI's o1. It provides a detailed approach for yewiki.org training such models utilizing large-scale support learning techniques.
DeepSeek-V3 Technical Report (December 2024) This report discusses the implementation of an FP8 mixed accuracy training framework verified on an extremely massive model, attaining both sped up training and reduced GPU memory use.
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism (January 2024) This paper delves into scaling laws and presents findings that help with the scaling of massive designs in open-source setups. It presents the DeepSeek LLM project, committed to advancing open-source language models with a long-lasting viewpoint.
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 variety of open-source code models trained from scratch on 2 trillion tokens. The models are pre-trained on a high-quality 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 characterized by affordable training and efficient reasoning.
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 model that attains performance comparable to GPT-4 Turbo in code-specific jobs.

Interesting occasions

- Hong Kong University duplicates R1 outcomes (Jan 25, '25).
- Huggingface announces huggingface/open-r 1: Fully open reproduction of DeepSeek-R1 to duplicate R1, totally open source (Jan 25, '25).
- OpenAI scientist confirms the DeepSeek group separately found and used some core ideas the OpenAI group used en route to o1

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