Understanding DeepSeek R1
Notes from our session on DeepSeek R1 - part 1
We’ve been tracking the explosive rise of DeepSeek R1, which has taken the AI world by storm in recent weeks. In this session, we dove deep into the evolution of the DeepSeek family - from the early models through DeepSeek V3 to the breakthrough R1. We also explored the technical innovations that make R1 so special in the world of open-source AI.
The DeepSeek Family Tree: From V3 to R1
DeepSeek isn’t just a single model; it’s a family of increasingly sophisticated AI systems. The evolution goes something like this:
DeepSeek V2:
This was the foundation model which leveraged a mixture-of-experts architecture, where only a subset of experts are used at inference, drastically improving the processing time for each token. It also featured multi-head latent attention to reduce memory footprint.
DeepSeek V3:
This model introduced FP8 training techniques, which helped drive down training costs by over 42.5% compared to previous iterations. FP8 is a less precise way to store weights inside the LLMs but can greatly improve the memory footprint. However, training using FP8 can typically be unstable, and it is hard to obtain the desired training results. Nevertheless, DeepSeek uses multiple tricks and achieves remarkably stable FP8 training. V3 set the stage as a highly efficient model that was already cost-effective (with claims of being 90% cheaper than some closed-source alternatives).
DeepSeek R1-Zero:
With V3 as the base, the team then introduced R1-Zero, the first reasoning-focused iteration. Here, the focus was on teaching the model not just to generate answers but to “think” before answering. Using pure reinforcement learning, the model was encouraged to generate intermediate reasoning steps, for example, taking extra time (often 17+ seconds) to work through a simple problem like “1+1.”
The key innovation here was the use of group relative policy optimization (GROP). Instead of relying on a conventional process reward model (which would have required annotating every step of the reasoning), GROP compares multiple outputs from the model. By sampling several potential answers and scoring them (using rule-based measures like exact match for math or verifying code outputs), the system learns to favor reasoning that leads to the correct result without the need for explicit supervision of every intermediate thought.
DeepSeek R1:
Recognizing that R1-Zero’s unsupervised approach produced reasoning outputs that could be hard to read or even mix languages, the developers went back to the drawing board. They used the raw outputs from R1-Zero to generate “cold start” data and then manually curated these examples to filter and improve the quality of the reasoning. This human post-processing was then used to fine-tune the original DeepSeek V3 model further—combining both reasoning-oriented reinforcement learning and supervised fine-tuning. The result is DeepSeek R1: a model that now produces readable, coherent, and reliable reasoning while still maintaining the efficiency and cost-effectiveness of its predecessors.
What Makes R1 Series Special?
The most fascinating aspect of R1 (zero) is how it developed reasoning capabilities without explicit supervision of the reasoning process. It can be further improved by using cold-start data and supervised reinforcement learning to produce readable reasoning on general tasks. Here's what sets it apart:
Open Source & Efficiency:
R1 is open source, allowing researchers and developers to inspect and build upon its innovations. Its cost efficiency is a major selling point especially when compared to closed-source models (claimed 90% cheaper than OpenAI) that require massive compute budgets.
Novel Training Approach:
Instead of relying solely on annotated reasoning (which is both expensive and time-consuming), the model was trained using an outcome-based approach. It started with easily verifiable tasks, such as math problems and coding exercises, where the correctness of the final answer could be easily measured.
By using group relative policy optimization, the training process compares multiple generated answers to determine which ones meet the desired output. This relative scoring mechanism allows the model to learn “how to think” even when intermediate reasoning is generated in a freestyle manner.
Overthinking?
An interesting observation is that DeepSeek R1 sometimes “overthinks” simple problems. For example, when asked “What is 1+1?” it might spend nearly 17 seconds evaluating different scenarios—even considering binary representations—before concluding with the correct answer. This self-questioning and verification process, although it might seem inefficient at first glance, could prove advantageous in complex tasks where deeper reasoning is necessary.
Prompt Engineering:
Traditional few-shot prompting techniques, which have worked well for many chat-based models, can actually degrade performance with R1. The developers recommend using direct problem statements with a zero-shot approach that specifies the output format clearly. This ensures that the model isn’t led astray by extraneous examples or hints that might interfere with its internal reasoning process.
Getting Started with R1
For those looking to experiment:
Smaller variants (7B-8B) can run on consumer GPUs or even only CPUs
Larger versions (600B) require significant compute resources
Available through major cloud providers
Can be deployed locally via Ollama or vLLM
Looking Ahead
We're particularly intrigued by several implications:
The potential for this approach to be applied to other reasoning domains
Impact on agent-based AI systems traditionally built on chat models
Possibilities for combining with other supervision techniques
Implications for enterprise AI deployment
Open Questions
How will this affect the development of future reasoning models?
Can this approach be extended to less verifiable domains?
What are the implications for multi-modal AI systems?
We'll be watching these developments closely, particularly as the community begins to experiment with and build upon these techniques.
Resources
Join our Slack community for ongoing discussions and updates about DeepSeek and other AI developments. We're seeing fascinating applications already emerging from our bootcamp participants working with these models.
Chat with DeepSeek:
https://www.deepseek.com/
Papers:
Blog Posts:
Cloud Providers:
Q&A
Q1: Which model deserves more attention – DeepSeek or Qwen2.5Max?
A: While Qwen2.5 is also a strong model in the open-source community, the choice ultimately depends on your use case. DeepSeek R1 emphasizes advanced reasoning and a novel training approach that may be especially valuable in tasks where verifiable logic is critical.
Q2: Why did major providers like OpenAI opt for supervised fine-tuning rather than reinforcement learning (RL) like DeepSeek?
A: We should note upfront that they do use RL at the very least in the form of RLHF. It is very likely that models from major providers that have reasoning capabilities already use something similar to what DeepSeek has done here, but we can’t be sure. It is also likely that due to access to more resources, they favored supervised fine-tuning due to its stability and the ready availability of large annotated datasets. Reinforcement learning, although powerful, can be less predictable and harder to control. DeepSeek’s approach innovates by applying RL in a reasoning-oriented manner, enabling the model to learn effective internal reasoning with only minimal process annotation - a strategy that has proven promising despite its complexity.
Q3: Did DeepSeek use test-time compute strategies similar to those of OpenAI?
A: DeepSeek R1’s design emphasizes efficiency by leveraging techniques such as the mixture-of-experts approach, which activates only a subset of parameters, to reduce compute during inference. This focus on efficiency is central to its cost advantages.
Q4: What is the difference between R1-Zero and R1?
A: R1-Zero is the initial model that learns reasoning solely through reinforcement learning without explicit process supervision. It generates intermediate reasoning steps that, while sometimes raw or mixed in language, serve as the foundation for learning. DeepSeek R1, on the other hand, refines these outputs through human post-processing and supervised fine-tuning. In essence, R1-Zero provides the unsupervised “spark,” and R1 is the polished, more coherent version.
Q5: How can one stay updated with in-depth, technical research while managing a busy schedule?
A: Staying current involves a combination of actively engaging with the research community (like AISC - see link to join slack above), following preprint servers like arXiv, attending relevant conferences and webinars, and participating in discussion groups and newsletters. Continuous engagement with online communities and collaborative research projects also plays a key role in keeping up with technical advancements.
Q6: In what use-cases does DeepSeek outperform models like O1?
A: The short answer is that it’s too early to tell. DeepSeek R1’s strength, however, lies in its robust reasoning capabilities and its efficiency. It is particularly well suited for tasks that require verifiable logic—such as mathematical problem solving, code generation, and structured decision-making—where intermediate reasoning can be reviewed and confirmed. Its open-source nature further allows for customized applications in research and enterprise settings.
Q7: What are the implications of DeepSeek R1 for enterprises and start-ups?
A: The open-source and cost-efficient design of DeepSeek R1 lowers the entry barrier for deploying advanced language models. Enterprises and start-ups can leverage its advanced reasoning for agentic applications ranging from automated code generation and customer support to data analysis. Its flexible deployment options—on consumer hardware for smaller models or cloud platforms for larger ones—make it an attractive alternative to proprietary solutions.
Q8: Will the model get stuck in a loop of “overthinking” if no correct answer is found?
A: While DeepSeek R1 has been observed to “overthink” simple problems by exploring multiple reasoning paths, it incorporates stopping criteria and evaluation mechanisms to prevent infinite loops. The reinforcement learning framework encourages convergence toward a verifiable output, even in ambiguous cases.
Q9: Is DeepSeek V3 completely open source, and is it based on the Qwen architecture?
A: Yes, DeepSeek V3 is open source and served as the foundation for later iterations. It is built on its own set of innovations—including the mixture-of-experts approach and FP8 training—and is not based on the Qwen architecture. Its design emphasizes efficiency and cost reduction, setting the stage for the reasoning innovations seen in R1.
Q10: How does DeepSeek R1 perform on vision tasks?
A: DeepSeek R1 is a text-based model and does not incorporate vision capabilities. Its design and training focus solely on language processing and reasoning.
Q11: Can professionals in specialized fields (for example, labs working on cures) apply these methods to train domain-specific models?
A: Yes. The innovations behind DeepSeek R1—such as its outcome-based reasoning training and efficient architecture—can be adapted to various domains. Researchers in fields like biomedical sciences can tailor these methods to build models that address their specific challenges while benefiting from lower compute costs and robust reasoning capabilities. It is likely that in deeply specialized fields, however, there will still be a need for supervised fine-tuning to get reliable results.
Q12: Were the annotators for the human post-processing experts in technical fields like computer science or mathematics?
A: The discussion indicated that the annotators primarily focused on domains where correctness is easily verifiable—such as math and coding. This suggests that expertise in technical fields was indeed leveraged to ensure the accuracy and clarity of the reasoning data.
Q13: Could the model get things wrong if it relies on its own outputs for learning?
A: While the model is designed to optimize for correct answers via reinforcement learning, there is always a risk of errors—especially in ambiguous scenarios. However, by evaluating multiple candidate outputs and reinforcing those that lead to verifiable results, the training process minimizes the likelihood of propagating incorrect reasoning.
Q14: How are hallucinations minimized in the model given its iterative reasoning loops?
A: The use of rule-based, verifiable tasks (such as math and coding) helps anchor the model’s reasoning. By comparing multiple outputs and using group relative policy optimization to reinforce only those that yield the correct result, the model is guided away from generating unfounded or hallucinated information.
Q15: Does the model rely on complex vector mathematics?
A: Yes, advanced techniques—including complex vector math—are integral to the implementation of mixture-of-experts and attention mechanisms in DeepSeek R1. However, the primary focus is on using these techniques to enable effective reasoning rather than showcasing mathematical complexity for its own sake.
Q16: Some worry that the model’s “thinking” may not be as refined as human reasoning. Is that a valid concern?
A: Early iterations like R1-Zero did produce raw and sometimes hard-to-read reasoning. However, the subsequent refinement process—where human experts curated and improved the reasoning data—has significantly enhanced the clarity and reliability of DeepSeek R1’s internal thought process. While it remains an evolving system, iterative training and feedback have led to meaningful improvements.
Q17: Which model variants are suitable for local deployment on a laptop with 32GB of RAM?
A: For local testing, a medium-sized model—typically in the range of 7B to 8B parameters—is recommended. Larger models (for example, those with hundreds of billions of parameters) require significantly more computational resources and are better suited for cloud-based deployment.
Q18: Is DeepSeek R1 “open source” or does it offer only open weights?
A: DeepSeek R1 is provided with open weights, meaning that its model parameters are publicly accessible. This aligns with the overall open-source philosophy, allowing researchers and developers to further explore and build upon its innovations.
Q19: What would happen if the order of training were reversed—starting with supervised fine-tuning before unsupervised reinforcement learning?
A: The current approach allows the model to first explore and generate its own reasoning patterns through unsupervised RL, and then refine these patterns with supervised methods. Reversing the order might constrain the model’s ability to discover diverse reasoning paths, potentially limiting its overall performance in tasks that benefit from autonomous thought.
ACKNOWLEDGEMENT: These notes are prepared by Mohsin Iqbal and edited by Boqi (Percy) Chen and myself.