DeepSeek Paper Summary | Programming Ocean Academy

A concise overview of "DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning"

DeepSeek-R1: Incentivizing Reasoning in LLMs

By the DeepSeek-AI Team

Abstract

DeepSeek-R1 is a novel reasoning model developed via reinforcement learning (RL). It demonstrates superior reasoning performance through RL without supervised fine-tuning (SFT), overcoming challenges in readability and language mixing with multi-stage training and distilled smaller models.

Key Highlights

  • First pure RL-based reasoning model without SFT.
  • Cold-start data improves performance and readability.
  • Performance surpasses OpenAI-o1-mini on benchmarks.
  • Distillation enables smaller models with strong reasoning.

Methodology

The model uses Group Relative Policy Optimization (GRPO) for RL, cold-start data for readability, and rejection sampling for improved supervised fine-tuning. Distillation transfers reasoning capabilities to smaller models.

Results and Key Findings

  • Achieved 79.8% on AIME 2024 (Pass@1) and 97.3% on MATH-500.
  • Outperformed baseline models on reasoning benchmarks.
  • Distilled 32B model set new records for dense models.

Applications and Impacts

DeepSeek-R1 is applicable in reasoning-intensive tasks, including mathematics, coding, and logic, with potential benefits for education and AI-driven search systems.

Conclusion

DeepSeek-R1 advances LLM reasoning through RL and sets benchmarks in reasoning tasks. Distillation ensures smaller models remain competitive, paving the way for future developments in adaptive AI systems.