The Security Gap in LLM Safety Measures
Why Reinforcement Learning falls short in DeepSeek-R1 models
This research examines the limitations of Reinforcement Learning (RL) as a strategy for ensuring harmlessness in advanced Large Language Models, specifically DeepSeek-R1.
- RL improves reasoning but faces challenges in consistently preventing harmful outputs
- Supervised Fine-Tuning (SFT) provides an alternative approach with different security trade-offs
- DeepSeek-R1 models highlight persistent vulnerabilities despite current safety measures
- Findings suggest the need for multi-layered safety strategies beyond RL alone
Why it matters: As LLMs become more powerful and widely deployed, understanding the gaps in current safety mechanisms is critical for security professionals to develop robust safeguards against potential misuse or harmful outputs.