You may have read about Deepseek in the news and wondered what the hell it was all about. There are many questions that have arisen as to what Deepseek did differently and what technology they used to achieve such incredible efficiency in training the R1 model.
Fortunately, being open source, the team has been able to answer many of these questions.
But many of us do not have the time to go through it all. So here is a "distilled version" of 20 of the most common questions and answers, with more or less technical details.
https://arxiv.org/abs/2501.12948
https://github.com/deepseek-ai/DeepSeek-V3/blob/main/DeepSeek_V3.pdf
Below are 20 sample FAQs focused on DeepSeek-R1 (and DeepSeek-R1-Zero) without directly comparing them to “conventional training techniques.” Each question addresses a specific aspect of the work, from motivation and pipeline details to real-world considerations.
1. What is the primary goal of the DeepSeek-R1 project?
DeepSeek-R1 aims to improve a language model’s reasoning capability—particularly in areas like math, coding, and complex Q&A—using large-scale reinforcement learning (RL). By focusing on incentivizing detailed, step-by-step thinking, the project seeks to create models that not only provide answers but also demonstrate richer reasoning skills.
2. Why does the project introduce two variants: DeepSeek-R1-Zero and DeepSeek-R1?
- DeepSeek-R1-Zero: Shows that a model can learn strong reasoning behaviors purely from RL signals, starting directly from a base model without any hand-curated supervised data.
- DeepSeek-R1: Uses a small “cold-start” dataset plus a multi-stage pipeline (mixing RL and supervised fine-tuning) to achieve higher readability, alignment, and accuracy.
3. What kind of tasks were the models trained to solve?
They were primarily trained on math (like AIME 2024, MATH-500), coding (Codeforces, LiveCodeBench), and logic/knowledge-intensive Q&A. These tasks have clearer correctness criteria, which makes it easier to apply automated reward signals during RL.
4. How does large-scale RL help a model develop chain-of-thought reasoning?
During RL, the model receives positive rewards when it produces correct or well-formatted reasoning. Over many iterations, it discovers that generating coherent, logically consistent intermediate steps (i.e., a detailed chain of thought) often leads to higher cumulative rewards.
5. What is the role of ‘cold-start’ data in DeepSeek-R1?
“Cold-start” data are a small collection of supervised examples containing clear and detailed reasoning steps. By fine-tuning on these before RL, the model has a foundation of well-structured solutions. This helps it produce readable and consistent chain-of-thought from the beginning of the RL phase.
6. How does DeepSeek-R1 improve upon DeepSeek-R1-Zero’s readability issues?
DeepSeek-R1’s pipeline introduces:
- A small initial batch of chain-of-thought data with human-friendly formatting.
- Additional supervised fine-tuning after RL to filter out unreadable or mixed-language solutions. These steps guide the model to maintain coherent language and structure.
7. What is the significance of the ‘aha moment’ mentioned for DeepSeek-R1-Zero?
The “aha moment” refers to a spontaneous behavior where the model rethinks a solution mid-way, effectively backtracking to correct earlier mistakes. This emerged naturally during RL, showing that the model was learning to reflect and refine its reasoning without explicit human instructions.
8. How does the paper evaluate correctness of math or coding solutions?
They rely on rule-based evaluators:
- For math, generated answers are checked against the correct numeric or symbolic solution.
- For coding, the code is compiled and tested against known test cases. Accurate outputs earn higher rewards, helping the model learn valid solutions.
9. Why do they incorporate a second RL stage in DeepSeek-R1?
After the model has focused on high-level reasoning tasks (like math/coding), they run another RL pass with a broader set of prompts to optimize for helpfulness, harmlessness, and consistent formatting. This “all-scenarios” RL helps align the model’s overall behavior to user-friendly standards.
10. What does distillation achieve in the DeepSeek-R1 pipeline?
Distillation transfers the reasoning skills of a larger or more advanced model (DeepSeek-R1) to a smaller backbone. By simply fine-tuning the smaller model on solutions generated by DeepSeek-R1, the smaller model inherits advanced reasoning strategies quickly and at lower training cost.
11. Do these models only generate chains-of-thought for math and coding tasks?
No. Although math and coding tasks offer clear correctness checks, DeepSeek-R1 is also trained on other domains (writing, open-domain Q&A, factual knowledge). Its chain-of-thought reasoning can be beneficial for any tasks requiring multi-step logical reasoning.
12. How big is the ‘cold-start’ dataset, and why does it help?
They mention “thousands” of curated examples for the initial fine-tuning. Even a relatively small but carefully prepared dataset helps the model format its internal reasoning clearly and speeds up RL convergence by providing better starting trajectories.
13. What types of reward signals are used?
They mainly use:
- Correctness rewards: The model is rewarded when solutions pass math checks or code test cases.
- Format rewards: Incentivize the model to place its internal reasoning and final answer in clearly marked sections or maintain language consistency.
14. How do the models ensure safety or avoid harmful outputs?
In the final RL phase, the authors include prompts from many scenarios (helpfulness, harmlessness, factual correctness) and use preference models to nudge outputs away from harmful or policy-violating content. This ensures the model’s chain-of-thought also avoids problematic text.
15. How do these models differ in performance on very long inputs (e.g., big code snippets)?
They mention that DeepSeek-R1 does well on longer contexts because it frequently relies on deeper chain-of-thought. However, extremely long contexts can still challenge any model, so the paper highlights future improvements on handling longer sequences more effectively.
16. Why might the model mix multiple languages inadvertently?
Without any prior constraints, the RL training sometimes merges languages while producing chain-of-thought if it sees multi-lingual training data. DeepSeek-R1 addresses this by adding a small penalty (or reduced reward) when it detects undesirable mixing.
17. What is the impact on inference time when generating long chain-of-thought?
Generating a detailed reasoning trace does require more tokens, so it can slow down response time. However, the paper’s results show that this trade-off drastically boosts correctness. Users who want quick answers can request a shorter chain-of-thought, balancing speed and interpretability.
18. How do the researchers evaluate final performance on tricky math or code tasks?
They typically run a pass@k metric:
- Generate multiple samples of solutions.
- See if at least one solution is correct. They also test a consensus or majority-vote method, where repeated solutions are checked for agreement to boost reliability.
19. Can these chain-of-thoughts be hidden from end-users, if desired?
Yes. Models can generate an internal chain-of-thought that remains hidden, then provide just the concise final answer. Practitioners sometimes prefer the chain-of-thought for debugging or interpretability, but it’s optional to display it publicly.
20. What future improvements do the authors propose for DeepSeek-R1?
They plan to extend the approach to:
- Enhance software-engineering tasks (e.g., more thorough code generation with advanced RL or asynchronous feedback).
- Support multi-step, multi-turn interactions.
- Improve multilingual handling to avoid mixing languages.
- Adapt chain-of-thought to other domains where thorough reasoning is beneficial.
These questions focus on why and how DeepSeek-R1 (and DeepSeek-R1-Zero) works—without directly comparing it to older or more conventional training methods. They address what the models do, how they are trained, the challenges they face, and the improvements or features that make them stand out in a reasoning-focused framework.
Wrap-Up
I hope that these new achievements will make AI more accessible and affordable to a wider audience, and not just to the wealthy nations and technology tycoons like OpenAI and Anthropic. Let's wait and see.
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