DeepSeek-R1 is an open-source language design 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 design in many benchmarks, but it also features completely MIT-licensed weights. This marks it as the very first non-OpenAI/Google model to deliver strong reasoning abilities in an open and available manner.

What makes DeepSeek-R1 particularly exciting is its transparency. Unlike the less-open methods from some market leaders, DeepSeek has actually released a detailed training methodology in their paper.
The model is also incredibly economical, 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, wiki.dulovic.tech the typical knowledge was that better models needed more data and calculate. While that's still valid, designs like o1 and R1 show an option: inference-time scaling through reasoning.

The Essentials

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

DeepSeek-R1 utilizes 2 significant concepts:

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

R1 and R1-Zero are both thinking models. This essentially implies they do Chain-of-Thought before answering. For the R1 series of designs, this takes type 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 supervised fine-tuning (SFT). RL is used to enhance the model's policy to optimize benefit.
R1-Zero attains exceptional accuracy however often produces confusing outputs, such as mixing numerous languages in a single action. R1 repairs that by incorporating minimal supervised fine-tuning and multiple RL passes, which improves both correctness and readability.

It is intriguing how some languages may express certain concepts 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 profoundly fascinating. It showcases how they developed such strong thinking designs, and what you can get out of each phase. This includes the issues that the resulting designs from each phase have, and how they fixed it in the next phase.

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

The typical training method: Pretraining on big dataset (train to forecast next word) to get the base design → supervised fine-tuning → choice tuning by means of 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 couple of thousand Chain-of-Thought (CoT) samples to guarantee the RL process has a good starting point. This gives a great model to start RL.
First RL Stage: Apply GRPO with rule-based benefits to improve reasoning accuracy and format (such as requiring chain-of-thought into thinking tags). When they were near convergence in the RL process, they transferred to the next action. The outcome of this step is a strong reasoning design but with weak basic capabilities, e.g., poor format and language blending.
Rejection Sampling + general data: Create brand-new SFT information through rejection tasting on the RL checkpoint (from step 2), combined with monitored data from the DeepSeek-V3-Base model. They collected around 600k high-quality reasoning samples.
Second Fine-Tuning: Fine-tune DeepSeek-V3-Base again on 800k total samples (600k thinking + 200k basic tasks) for more comprehensive capabilities. This step resulted in a strong reasoning model with basic abilities.
Second RL Stage: Add more benefit signals (helpfulness, harmlessness) to refine the last model, in addition to the reasoning benefits. The result is DeepSeek-R1.
They also did model distillation for numerous Qwen and Llama designs on the thinking traces to get distilled-R1 models.

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

Group Relative Policy Optimization (GRPO)

The standard idea behind using reinforcement knowing for LLMs is to tweak the model's policy so that it naturally produces more precise and helpful answers.
They used a reward system that examines not only for correctness but also for proper formatting and asteroidsathome.net language consistency, so the model gradually finds out to favor actions that fulfill these quality criteria.

In this paper, they encourage the R1 model to produce chain-of-thought reasoning through RL training with GRPO.
Rather than adding 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 especially interesting is its dependence on straightforward, rule-based benefit functions.
Instead of depending on expensive external designs or human-graded examples as in conventional RLHF, the RL used for R1 uses basic requirements: it may offer a higher reward if the response is correct, if it follows the anticipated/ formatting, and if the language of the response matches that of the timely.
Not counting on a reward design also implies you do not need to hang out and effort training it, and it doesn't take memory and calculate away from your main design.

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

1. For each input timely, the model produces different responses.
2. Each reaction receives a scalar reward based on aspects like precision, formatting, and language consistency.
3. Rewards are changed relative to the group's performance, essentially determining just how much better each response is compared to the others.
4. The design updates its technique somewhat to prefer responses with greater relative benefits. It only makes minor adjustments-using techniques like clipping and a KL penalty-to guarantee the policy does not stray too far from its initial behavior.

A cool element of GRPO is its flexibility. You can use simple rule-based reward functions-for circumstances, awarding a reward when the design properly utilizes 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 quite a great application of training an LLM with RL using GRPO. GRPO has likewise already been included to the Transformer Reinforcement Learning (TRL) library, which is another good resource.
Finally, Yannic Kilcher has an excellent video explaining GRPO by going through the DeepSeekMath paper.

Is RL on LLMs the path to AGI?

As a final note on explaining DeepSeek-R1 and the methods they've provided 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 show that RL enhances the design's general performance by rendering the output distribution more robust, simply put, it appears that the enhancement is credited to boosting the correct response from TopK instead of the enhancement of fundamental abilities.

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

This suggests that support learning on LLMs is more about refining and "shaping" the existing circulation of actions rather than endowing the model with completely brand-new abilities.
Consequently, while RL techniques such as PPO and GRPO can produce substantial efficiency gains, there appears to be an intrinsic ceiling determined by the underlying design'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 turning point. I'm delighted to see how it unfolds!

Running DeepSeek-R1

I have actually utilized DeepSeek-R1 via the main chat interface for various problems, which it appears to resolve all right. The extra search performance 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 math than o3-mini.

I also 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 objective was to see how the model would perform when released on a single H100 GPU-not to thoroughly check the design's capabilities.

671B by means of 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 by means of llama.cpp:

29 layers seemed to be the sweet spot provided this configuration.

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 regional video gaming setup.
Digital Spaceport wrote a complete guide on how to run Deepseek R1 671b totally 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 models on available hardware.

What matters most to me is a mix of usefulness and time-to-usefulness in these designs. Since thinking designs require to think before answering, their time-to-usefulness is normally greater than other designs, but their usefulness is also normally higher.
We need to both optimize effectiveness and reduce time-to-usefulness.

70B through Ollama

70.6 b params, 4-bit KM quantized DeepSeek-R1 running through 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 through 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 dish to replicate 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 unifies multimodal understanding and generation. It can both understand and produce images.
DeepSeek-R1: Incentivizing Reasoning Capability in Large Language Models through Reinforcement Learning (January 2025) This paper introduces DeepSeek-R1, an open-source reasoning model that measures up to the efficiency of OpenAI's o1. It presents a detailed approach for training such designs utilizing massive support learning techniques.
DeepSeek-V3 Technical Report (December 2024) This report discusses the application of an FP8 mixed precision training framework validated on an exceptionally massive design, attaining both accelerated training and lowered GPU memory usage.
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism (January 2024) This paper dives into scaling laws and provides findings that help with the scaling of large-scale designs in open-source configurations. It introduces the DeepSeek LLM job, 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 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 high-quality project-level code corpus and employ a fill-in-the-blank task 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 effective 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 efficiency comparable to GPT-4 Turbo in code-specific tasks.

Interesting events

- Hong Kong University reproduces R1 outcomes (Jan 25, '25).
- Huggingface reveals huggingface/open-r 1: Fully open recreation of DeepSeek-R1 to duplicate R1, totally open source (Jan 25, '25).
- OpenAI scientist verifies the DeepSeek team independently found and utilized some core ideas the OpenAI team used en route to o1

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