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 design in many standards, but it also features completely MIT-licensed weights. This marks it as the first non-OpenAI/Google design to deliver strong reasoning capabilities in an open and available manner.

What makes DeepSeek-R1 especially amazing is its transparency. Unlike the less-open approaches from some market leaders, DeepSeek has published a detailed training methodology in their paper. The design is also remarkably 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 wisdom was that much better models needed more information and calculate. While that's still legitimate, designs like o1 and R1 show an option: inference-time scaling through reasoning.

The Essentials

The DeepSeek-R1 paper provided multiple designs, however main among them were R1 and R1-Zero. Following these are a series of distilled designs that, while intriguing, I will not discuss here.

DeepSeek-R1 utilizes 2 significant ideas:

1. A multi-stage pipeline where a small set of cold-start data kickstarts the model, followed by large-scale RL.

1. Group Relative Policy Optimization (GRPO), a reinforcement knowing method that counts on comparing numerous model outputs per prompt to avoid the requirement for a separate critic.
   
   R1 and R1-Zero are both reasoning models. This basically implies they do Chain-of-Thought before answering. For the R1 series of designs, this takes type as thinking within a tag, before answering with a last summary.
   
   R1-Zero vs R1
   
   R1-Zero uses Reinforcement Learning (RL) straight to DeepSeek-V3-Base with no supervised fine-tuning (SFT). RL is utilized to optimize the model's policy to make the most of reward. R1-Zero attains exceptional precision however in some cases produces confusing outputs, such as mixing multiple languages in a single action. R1 repairs that by incorporating restricted supervised fine-tuning and multiple RL passes, which enhances both correctness and readability.
   
   It is fascinating how some languages might express certain concepts better, which leads the model to pick the most expressive language for the job.
   
   Training Pipeline
   
   The training pipeline that DeepSeek released in the R1 paper is profoundly interesting. It showcases how they developed such strong reasoning designs, and what you can anticipate from each stage. This consists of the issues that the resulting models from each phase have, and how they solved it in the next stage.
   
   It's fascinating that their training pipeline differs from the usual:
   
   The typical training method: Pretraining on large dataset (train to predict next word) to get the base model → monitored fine-tuning → choice tuning via 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 guarantee the RL procedure has a decent starting point. This offers an excellent model to start RL. First RL Stage: Apply GRPO with rule-based rewards to enhance reasoning correctness and format (such as forcing chain-of-thought into thinking tags). When they were near merging in the RL process, they moved to the next action. The outcome of this action is a strong thinking design but with weak basic abilities, e.g., bad format and language blending. Rejection Sampling + general information: Create new SFT information through rejection tasting on the RL checkpoint (from action 2), elearnportal.science integrated with monitored data 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 total samples (600 + 200k general jobs) for wider abilities. This action led to a strong thinking design with basic capabilities. Second RL Stage: Add more reward signals (helpfulness, harmlessness) to improve the last model, in addition to the thinking benefits. The outcome 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 design to improve a trainee design by creating training information for the trainee design. The teacher is generally a larger model than the trainee.
   
   Group Relative Policy Optimization (GRPO)
   
   The fundamental concept 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 benefit system that examines not just for accuracy however also for correct formatting and language consistency, so the design slowly discovers to prefer responses that satisfy 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 inference time, the training process itself pushes the design to produce detailed, detailed outputs-making the chain-of-thought an emergent behavior of the optimized policy.
   
   What makes their technique particularly intriguing is its dependence on straightforward, rule-based benefit functions. Instead of depending upon expensive external designs or human-graded examples as in traditional RLHF, the RL used for R1 utilizes basic criteria: it may give a greater benefit if the answer is correct, if it follows the anticipated/ format, and if the language of the response matches that of the timely. Not counting on a reward model also means you do not have to hang around and effort training it, and it doesn't take memory and compute 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 reactions.
2. Each reaction receives a scalar benefit based upon aspects like precision, formatting, and language consistency.
3. Rewards are adjusted relative to the group's efficiency, essentially measuring just how much better each response is compared to the others.
4. The design updates its technique a little to prefer responses with greater relative benefits. It just makes small adjustments-using techniques like clipping and a KL penalty-to make sure the policy doesn't wander off too far from its initial habits.
   
   A cool element of GRPO is its versatility. You can use simple rule-based reward functions-for circumstances, granting a bonus offer when the model correctly uses the syntax-to guide the training.
   
   While DeepSeek utilized GRPO, you could utilize alternative methods instead (PPO or PRIME).
   
   For those aiming to dive deeper, Will Brown has actually written rather a great implementation of training an LLM with RL using GRPO. GRPO has also currently been added to the Transformer Reinforcement Learning (TRL) library, which is another excellent resource. Finally, Yannic Kilcher has a fantastic 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've presented in their paper, I desire to highlight a passage from the DeepSeekMath paper, based on a point Yannic Kilcher made in his video.
   
   These findings show that RL boosts the model's total performance by rendering the output circulation more robust, simply put, it seems that the enhancement is associated to increasing the proper action from TopK rather than the enhancement of fundamental abilities.
   
   To put it simply, RL fine-tuning tends to shape the output circulation so that the highest-probability outputs are most likely to be appropriate, even though the general ability (as determined by the diversity of appropriate answers) is mainly present in the pretrained model.
   
   This suggests that support knowing on LLMs is more about refining and "shaping" the existing circulation of actions instead of endowing the model with totally brand-new capabilities. Consequently, while RL methods such as PPO and GRPO can produce significant performance gains, there appears to be an inherent ceiling identified by the underlying model'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 milestone. I'm delighted to see how it unfolds!
   
   Running DeepSeek-R1
   
   I have actually used DeepSeek-R1 via the main chat user interface for various problems, which it appears to solve well enough. The extra search functionality makes it even better to use.
   
   Interestingly, o3-mini(-high) was launched as I was composing this post. From my initial screening, R1 appears more powerful at math than o3-mini.
   
   I also rented 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 design would carry out when released on a single H100 GPU-not to extensively test 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 running on the GPU), running through llama.cpp:
   
   29 layers seemed to be the sweet area provided this configuration.
   
   Performance:
   
   A r/localllama user explained that they had the ability to overcome 2 tok/sec with DeepSeek R1 671B, without using their GPU on their local gaming setup. Digital Spaceport composed a full guide on how to run Deepseek R1 671b fully 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 serious work, however it's enjoyable to run these large designs on available hardware.
   
   What matters most to me is a mix of effectiveness and time-to-usefulness in these designs. Since reasoning designs need to think before addressing, their time-to-usefulness is typically higher than other models, but their effectiveness is likewise normally higher. We need to both make the most of usefulness and minimize time-to-usefulness.
   
   70B through Ollama
   
   70.6 b params, 4-bit KM quantized DeepSeek-R1 running through Ollama:
   
   GPU utilization soars 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 fully regional "deep scientist" with DeepSeek-R1 - YouTube). DeepSeek R1's recipe to duplicate 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 grandma - 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 framework that unifies 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 introduces DeepSeek-R1, an open-source reasoning model that measures up to the efficiency of OpenAI's o1. It presents a detailed methodology for training such models using massive reinforcement knowing techniques. DeepSeek-V3 Technical Report (December 2024) This report discusses the implementation of an FP8 mixed precision training framework confirmed on an extremely large-scale design, attaining both sped up training and lowered GPU memory use. DeepSeek LLM: Scaling Open-Source Language Models with Longtermism (January 2024) This paper explores scaling laws and presents findings that facilitate the scaling of massive designs in open-source configurations. It introduces the DeepSeek LLM project, dedicated to advancing open-source language designs with a long-term viewpoint. DeepSeek-Coder: When the Large Language Model Meets Programming-The Rise of Code Intelligence (January 2024) This research introduces 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 job to boost 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 economical training and efficient inference. DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence (June 2024) This research study 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 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, completely open source (Jan 25, '25).
5. OpenAI scientist confirms the DeepSeek group separately discovered and utilized some core concepts the OpenAI group used en route to o1
   
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