arXiv:2402.06734v2 Announce Type: replace Abstract: We study data corruption robustness for reinforcement learning with human feedback (RLHF) in an offline setting. Given an offline dataset of pairs of trajectories along with feedback about human preferences, an $\varepsilon$-fraction of the pairs is corrupted (e.g., feedback flipped or trajectory features manipulated), capturing an adversarial attack or noisy human preferences. We aim to design algorithms that identify a near-optimal policy from the corrupted data, with provable guarantees. Existing theoretical works have separately studied t
The increasing deployment of AI systems in real-world contexts necessitates robust solutions for data corruption and adversarial attacks, especially with human feedback loops becoming critical.
This research addresses a fundamental vulnerability in AI systems, moving towards more reliable and secure autonomous agents and decision-making processes.
AI models can potentially become more resilient to noisy or malicious data inputs, improving their safety and trustworthiness in critical applications.
- · AI developers
- · Cybersecurity sector
- · Industries relying on AI decision-making (e.g., finance, defense)
- · Consumers of AI-driven services
- · Adversarial attackers
- · Entities benefiting from system vulnerabilities
More robust and trustworthy AI models will accelerate their integration into sensitive applications.
Increased trust in AI systems could lead to greater automation and delegation of complex tasks to AI agents.
The enhanced security of AI might shift resources from error-correction and oversight to innovation and new application development.
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