Stable-GFlowNet: Toward Diverse and Robust LLM Red-Teaming via Contrastive Trajectory Balance
arXiv:2605.00553v2 Announce Type: replace Abstract: Large Language Model (LLM) Red-Teaming, which proactively identifies vulnerabilities of LLMs, is an essential process for ensuring safety. Finding effective and diverse attacks in red-teaming is important, but achieving both is challenging. Generative Flow Networks (GFNs) that perform distribution matching are a promising methods, but they are notorious for training instability and mode collapse. In particular, unstable rewards in red-teaming accelerate mode collapse. We propose Stable-GFN (S-GFN), which eliminates partition function $Z$ esti
The rapid deployment and increasing capabilities of Large Language Models necessitate robust red-teaming techniques to proactively identify and mitigate vulnerabilities, especially as AI systems are integrated into critical applications.
Improved red-teaming methods are crucial for enhancing the safety, reliability, and trustworthiness of LLMs, which directly impacts their adoption and societal integration, mitigating risks of misuse or unintended consequences.
The development of more stable and effective red-teaming tools, like Stable-GFN, enables better identification of diverse and robust attack vectors against LLMs, leading to more secure and resilient AI systems.
- · AI developers
- · Cybersecurity researchers
- · AI safety organizations
- · Regulators
- · Malicious actors
- · Vulnerable LLMs
- · Unsophisticated red-teaming methods
Enhances the ability to find and fix vulnerabilities in large language models before they cause harm.
Accelerates the development of more robust and secure AI systems, increasing public and institutional trust in AI technologies.
Could influence future AI development paradigms, emphasizing safety and adversarial robustness as core design principles, potentially impacting regulatory frameworks and industry standards.
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