Challenges in Ensuring AI Safety in DeepSeek-R1 Models: The Shortcomings of Reinforcement Learning Strategies
This addresses safety issues in advanced AI models for developers and users, but it is incremental as it builds on existing methods.
The paper tackles the problem of ensuring harmlessness in DeepSeek-R1 models by identifying limitations of Reinforcement Learning (RL) strategies, such as reward hacking and generalization failures, and proposes hybrid training with Supervised Fine-Tuning (SFT) to improve safety.
Large Language Models (LLMs) have achieved remarkable progress in reasoning, alignment, and task-specific performance. However, ensuring harmlessness in these systems remains a critical challenge, particularly in advanced models like DeepSeek-R1. This paper examines the limitations of Reinforcement Learning (RL) as the primary approach for reducing harmful outputs in DeepSeek-R1 and compares it with Supervised Fine-Tuning (SFT). While RL improves reasoning capabilities, it faces challenges such as reward hacking, generalization failures, language mixing, and high computational costs. We propose hybrid training approaches combining RL and SFT to achieve robust harmlessness reduction. Usage recommendations and future directions for deploying DeepSeek-R1 responsibly are also presented.