Paper Reading: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Session led by Mike A

Join us for an insightful session on the groundbreaking paper, DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

In this session, we will explore DeepSeek-R1, a state-of-the-art reasoning model that pushes the boundaries of reinforcement learning (RL) applied to large language models (LLMs). This paper introduces two novel models, DeepSeek-R1-Zero and DeepSeek-R1, showcasing advancements in reasoning capabilities through RL-driven self-evolution. Unlike traditional models relying heavily on supervised fine-tuning (SFT), DeepSeek-R1-Zero develops its reasoning abilities purely through RL, while DeepSeek-R1 combines RL with a multi-stage training pipeline for enhanced performance.

The paper highlights exceptional benchmarks achieved by DeepSeek-R1 on math, coding, and STEM-related reasoning tasks, where its performance rivals that of leading closed-source models such as OpenAI's o1-1217. Additionally, we’ll discuss distilling these capabilities into smaller, more efficient models to make advanced reasoning accessible for diverse applications.

Whether you're a researcher, developer, or enthusiast in AI and LLMs, this paper reading will provide an in-depth understanding of the novel reinforcement learning techniques driving DeepSeek-R1 and its implications for the future of AI-driven reasoning systems. Don't miss this opportunity to engage with cutting-edge advancements in the field!

[2501.12948] DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

GitHub - deepseek-ai/DeepSeek-R1