DeepSeek-R1: RL for Reasoning in Generative AI

Paradigm Shift in Model Training: DeepSeek-R1-Zero innovatively employs pure Reinforcement Learning (RL) directly on a base model, bypassing the traditional supervised fine-tuning step, to cultivate reasoning abilities using rule-based rewards.
Open Source Transparency: The complete DeepSeek-R1 package, including the paper, training code, and evaluation suite, is publicly available on GitHub under the Apache 2.0 license, promoting transparency and collaborative progress.
Rule-Based Rewards and Structured Output: DeepSeek-R1 utilizes verifiable, interpretable rule-based rewards and structured output templates to encourage chain-of-thought reasoning, enhancing model clarity and control.
Illustrative Example - Gini Impurity RL: A Google Colab demo demonstrates the effectiveness of RL by training a small policy model to compute Gini impurity, showcasing the model's ability to reduce noise and converge to accurate results through RL.
Innovation Beyond RL: DeepSeek models incorporate innovations such as Mixture of Experts (MoE) architecture, enhancing their capabilities beyond standard language models.
Practical Considerations for Implementation: According to reactions, the suitability of DeepSeek models, particularly MoE architectures, for specific applications with large parameter sizes (e.g., 70B or 80B) requires thorough investigation and testing to match project parameters effectively.