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- DeepSeek-R1 - A Step Towards AGI [Paper Unfold]
DeepSeek-R1 - A Step Towards AGI [Paper Unfold]
Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
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Paper Unfold breakdown of complex research papers into easy-to-understand pointers.
What—the problem does this paper solve
How—it solves it
Why—it’s important
Where—it can be used in real-world
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Research Paper
DeepSeek-R1 is a family of LLMs that enhances reasoning capabilities through reinforcement learning.
The research explores how LLMs can develop reasoning skills without relying heavily on supervised fine-tuning data.
DeepSeek outperformed OpenAI’s o1.
The best part is it’s open source [GitHub].
Let’s dive in.
What—the problem does this paper solve
LLMs often struggle with reasoning, essential for solving complex tasks.
Traditional methods use supervised fine-tuning with labeled data, which takes time and resources.
Models trained with just reinforcement learning can have issues like poor readability and language mixing.
How—it solves it
3 methods mentioned in the paper:
pure reinforcement learning
reinforcement learning with cold start
distillation
1. Pure Reinforcement Learning (DeepSeek-R1-Zero)
trains directly with reinforcement learning without using labeled data first.
(The AI learns to solve math problems by trying and receiving feedback, not by copying examples)uses group relative policy optimization to optimize learning efficiently.
(Like teaching with smaller, focused exercises instead of random lessons)reward system focuses on-
accuracy so that answers are correct
format for clear reasoning with
<think>
and</think>
tags to separate thought processes
LLMs can develop skills like self-verification (checking their answers) and creating long chains of thought.
2. RL with Cold Start (DeepSeek-R1)
starts training with a small, high-quality dataset of examples, like detailed step-by-step solutions.
(Give the AI a few solved algebra problems before it learns to solve on its own)adds a language consistency reward to ensure answers stay in one language.
uses stages of training
fine-tuning on the starting dataset
RL for reasoning
combining rejection sampling (filtering bad answers) with supervised fine-tuning
final RL to refine
3. Distillation
transfers reasoning skills to smaller models by training them on the data generated by DeepSeek-R1.
creates powerful, smaller models that perform well with fewer resources.
Why—it’s important
shows LLMs can learn complex reasoning through RL, without heavy dependence on labeled data.
makes AI training less resource-intensive by minimizing the need for labeled data.
solves issues like poor readability and language mixing, making answers clearer.
open-sourcing the models helps researchers and developers improve AI further.
Where—it can be used in real-world
advanced AI tutors for subjects like math or science.
assisting with code generation, debugging, or solving by reasoning.
smaller models can work on devices like phones, reducing dependency on powerful servers.
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