用短期优化+长期评估防止智能体搞出人类看不懂的奖励欺骗行为
MONA: Myopic Optimization with Non-myopic Approval Can Mitigate Multi-step Reward Hacking
- 结合短视优化与远见奖励，避免复杂多步策略出现
- 在无需识别奖励欺骗的前提下，有效抑制多步奖励劫持
- 适用于大模型监督、推理编码及长程环境等对齐失效场景
未来先进的AI系统可能通过强化学习（RL）学会人类难以理解的复杂策略，存在安全风险。本文提出一种名为‘短视优化+非短视批准’（MONA）的训练方法，通过结合短视优化与远见奖励，即使人类无法察觉行为异常，也能避免智能体学习到高奖励但不期望的多步策略（即多步‘奖励劫持’）。实验在三种情境中验证了MONA的有效性：包含两步环境且由大语言模型（LLM）负责代理监督和编码推理，以及更长视野的网格世界环境，模拟传感器篡改等对齐失败模式。结果表明，MONA可在不依赖额外信息、不识别奖励劫持的情况下，成功防止普通强化学习引发的多步奖励劫持。
原文摘要 · Abstract (English)
Future advanced AI systems may learn sophisticated strategies through reinforcement learning (RL) that humans cannot understand well enough to safely evaluate. We propose a training method which avoids agents learning undesired multi-step plans that receive high reward (multi-step "reward hacks") even if humans are not able to detect that the behaviour is undesired. The method, Myopic Optimization with Non-myopic Approval (MONA), works by combining short-sighted optimization with far-sighted reward. We demonstrate that MONA can prevent multi-step reward hacking that ordinary RL causes, even without being able to detect the reward hacking and without any extra information that ordinary RL does not get access to. We study MONA empirically in three settings which model different misalignment failure modes including 2-step environments with LLMs representing delegated oversight and encoded reasoning and longer-horizon gridworld environments representing sensor tampering.
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