Challenges in Ensuring AI Safety in DeepSeek-R1 Models: The Shortcomings of Reinforcement Learning Strategies

• URL: http://arxiv.org/abs/2501.17030v1
• Date: Tue, 28 Jan 2025 15:52:51 GMT
• Title: Challenges in Ensuring AI Safety in DeepSeek-R1 Models: The Shortcomings of Reinforcement Learning Strategies
• Authors: Manojkumar Parmar, Yuvaraj Govindarajulu,
• Abstract summary: This paper examines the limitations of Reinforcement Learning as the primary approach for reducing harmful outputs in DeepSeek-R1.<n>We propose hybrid training approaches combining RL and Supervised Fine-Tuning to achieve robust harmlessness reduction.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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