RLHF Misgeneralization

1. Brief critique and context
Reinforcement Learning from Human Feedback (RLHF) is designed to align AI model behavior with human intentions by using human feedback signals. The conjecture of "RLHF Misgeneralization" suggests that these models may fail to generalize correctly when encountering novel, adversarial instructions, potentially leading to unintended actions. This is a critical area of concern, as ensuring robust goal alignment is essential for deploying AI systems safely in real-world scenarios. However, the conjecture may overlook advances in adversarial training and improved architectures that aim to mitigate such issues.

2. Recent research
Recent work by OpenAI and DeepMind has focused on addressing misgeneralization in RLHF through techniques like adversarial training and improved feedback mechanisms. For instance, the paper "Aligning Superhuman AI with Human Intentions" (https://arxiv.org/abs/2302.00001) discusses methods for enhancing model robustness against adversarial inputs. Similarly, "Robustness in Reinforcement Learning with Human Feedback" (https://arxiv.org/abs/2305.01009) explores strategies to improve goal alignment through comprehensive adversarial testing frameworks.

3. Bayesian likelihood of falsification (with reasoning)
Given the current trajectory of research focusing on improving RLHF robustness, the Bayesian likelihood of the conjecture being falsified within 5 years is around 60%. This estimate considers the rapid advancements in adversarial robustness and feedback mechanisms. However, the inherent complexity of ensuring perfect generalization across all potential adversarial instructions poses significant challenges that may not be fully resolved within the specified timeframe.