The DeepSeek R1 paper leaves a powerful lasting impression after reading.
While I recommend everyone read it, I suspect few actually will.
Today, I've summarized three highlights from the paper in an easy-to-understand way, hoping more people can grasp how important this paper is.
Highlight 1: Goodbye 'Question Banks', Pure 'Combat' Can Also Train Reasoning Masters!
When we study, don't we often 'grind questions'? We do a lot of practice problems to consolidate knowledge and improve problem-solving skills. Training AI models used to follow a similar routine: first 'feed' the AI a massive amount of 'practice problems' (supervised data) to let it learn knowledge and language, then perform 'special training' (fine-tuning) to enhance specific skills.
This 'grinding + special training' model seemed to be the 'standard operation' in the AI world.
However, the DeepSeek-AI team took an unconventional path. They wanted to see: could an AI skip the 'cram school' and improve its reasoning ability directly through 'actual combat' (Reinforcement Learning)?
They created a model called DeepSeek-R1-Zero. The most impressive thing about this model is that it didn't 'grind questions' at all; it went straight to the 'battlefield'—using Reinforcement Learning (RL) technology to train the base model.
What does that feel like? It's like training a basketball player not by having them memorize tactics and skills first, but by putting them directly on the court to constantly try, explore, and improve during the game!
And guess what? This seemingly 'wild' training method actually produced an AI model with incredible reasoning power! DeepSeek-R1-Zero performed stunningly in various reasoning tests and even displayed some unexpected 'superpowers':
'Self-Verification' Skill: After finishing a problem, the model 'looks back' to check if the answer is correct. If it finds a mistake, it corrects itself! This is just like a top student carefully verifying their work after an exam—so self-disciplined!
'Reflection' Skill: The model can 'reflect' on its own thinking process, analyzing what it did well and what it didn't. It's the AI version of 'learning and constantly reviewing'!
'Long Chain of Thought' (Long CoT): The model can generate very detailed problem-solving steps, showing its thinking process step-by-step. It's like a top student who not only gives the answer but writes out the entire process so you understand it at a glance!
More importantly, these reasoning abilities of DeepSeek-R1-Zero 'grew' purely through reinforcement learning without any help from 'grinding' data. It's like proving that even without 'cram schools,' the 'unorthodox' path can still produce a martial arts master if the method is right!
The success of DeepSeek-R1-Zero is a bombshell for AI research! It proves for the first time that AI reasoning can truly be 'triggered' through reinforcement learning without rigid 'question grinding.' This opens up new ideas: training AI can be this 'liberated'!
Highlight 2: 'Cold Start' + Multi-stage Training, Building a Stronger Reasoning 'Engine' DeepSeek-R1
Although DeepSeek-R1-Zero was already impressive, the DeepSeek-AI team wasn't satisfied. They wanted to go further and build a more powerful reasoning engine! They found that R1-Zero still had some minor flaws in practical application, such as:
'Incomprehensible reasoning processes': The model's reasoning was sometimes too 'jumpy' and not intuitive enough, like a genius's scratchpad that only they can understand.
'Language confusion': When dealing with complex problems, the model might mix Chinese and English, making it feel a bit 'split.'
To solve these problems and further enhance reasoning, the team launched the DeepSeek-R1 model. R1 is a comprehensive upgrade over R1-Zero, with the secret lying in 'Cold Start Data' and 'Multi-stage Training.'
'Cold Start Data' is like a 'preview' for the model, giving it a preliminary understanding of human reasoning. Researchers collected high-quality reasoning data to 'warm up' the base model, letting it grasp the reasoning style humans expect.
It's like an athlete doing warm-up exercises and stretching before a formal training session to get the body into the right state for high-intensity work.
After the 'warm-up,' DeepSeek-R1 enters the 'main event' of multi-stage reinforcement learning. This process is like 'leveling up,' improving the model's reasoning step-by-step:
'Reasoning-oriented RL': Based on the 'warmed-up' model, RL training focuses on hard tasks like math, coding, and logic—like hiring an 'International Math Olympiad gold medalist coach' to tutor the model.
'General Capability Development' (Rejection Sampling and Supervised Fine-Tuning): Once reasoning improves significantly, the model's own output is used to generate new high-quality 'practice problems.' Combined with problems from other fields (writing, Q&A, etc.), the model 'grinds' again to improve all-around skills. It's like making that 'Math Olympiad winner' compete in all subjects to become a well-rounded student!
'User Experience Optimization' (Reinforcement Learning for all Scenarios): After all-around scores improve, a second stage of RL training considers broader scenarios and user needs, making the model more 'down-to-earth,' useful, and considerate. It's like sending the 'all-around scholar' to social practice to improve their comprehensive quality and popularity!
Through this 'Cold Start' + 'Multi-stage Training' combo, DeepSeek-R1 not only solved R1-Zero's minor issues but also achieved a 'rocket-like' leap in reasoning. Experimental results show that DeepSeek-R1's performance in various reasoning tasks can now go toe-to-toe with OpenAI's top-tier o1-1217 model!
Highlight 3: Democratizing Reasoning Power, Small Models Can Have Great Wisdom!
Large language models are powerful, but with tens or hundreds of billions of parameters, they are like 'behemoths' that ordinary computers can't run and ordinary people can't afford. How can we let reasoning power 'fly into the homes of ordinary people'? The DeepSeek-AI team had a clever trick: Knowledge Distillation!
Knowledge distillation, simply put, is 'compressing' the knowledge and abilities of a 'Large Model Teacher' into a 'Small Model Student.' Using the 'Super Scholar' DeepSeek-R1 as a teacher, the team trained a group of 'Mini Scholars'—small models including 1.5B, 7B, 8B, 14B, 32B, and 70B versions.
Surprisingly, these 'Mini Scholars' exceeded expectations, outperforming other open-source models of the same size and even challenging some larger 'closed-source giants'! For example:
DeepSeek-R1-Distill-Qwen-7B (a 7B small model) outperformed QwQ-32B-Preview (a 32B large model) in the AIME 2024 test! It's a classic case of the 'underdog' winning.
DeepSeek-R1-Distill-Qwen-32B achieved excellent results in multiple tests, even rivaling OpenAI's o1-mini model! It's inspiring to see a 'mini scholar' reach 'top-tier high school' levels.
Most importantly, the DeepSeek-AI team has open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and these six 'Mini Scholar' models for free! This means ordinary people like us can use such powerful AI models for free—a truly 'conscientious' move! Researchers and developers can also build upon these open-source models to drive AI technology forward.
Summary and Outlook
The emergence of DeepSeek-R1 shows us more possibilities for improving AI reasoning. It proves the potential of the pure reinforcement learning route and points a new direction for building more powerful, practical, and accessible AI models.
In short, the birth of DeepSeek-R1 is a major milestone in AI history, showing us the dawn of AI 'thinking' and making us full of expectations for the future!
I hope this article gives you a preliminary understanding of DeepSeek-R1. If you're interested in AI or want more details, I highly recommend reading the original paper; you'll find even more surprises!
Author: Gemini 2.0 Flash Thinking Experimental 01-21
I wish this article were written by R1, which would be more interesting, but unfortunately, R1 cannot write this yet.
Google's new model is truly great.
