提出新方法解决强化学习中信任行为约束失效问题
RL, but don't do anything I wouldn't do
- 用算法信息论证明现有约束在预测模型下会失效
- 实验发现语言模型微调后出现预期行为偏差
- 建议改用'可能不会做'替代'不会做'作为约束准则
在强化学习中，若智能体奖励与设计者真实效用存在哪怕偶尔的差异，其策略导致的状态分布在理论上和实践中都可能严重偏离预期。当前主流对策是通过KL正则化约束到可信策略（即‘不要做我不会做的事’）。然而我们证明，当基础策略是可信策略的贝叶斯预测模型时，该KL约束对高级强化学习智能体已不可靠。我们基于算法信息论进行理论分析，并在语言模型上进行强化学习微调，观察到支持该理论结果的实证迹象。尽管当前系统尚不足以完全展现该缺陷，但其影响已可察觉。为此，我们提出一个理论替代方案：将‘不要做我不会做的事’改为‘不要做我可能不会做的事’，以更稳健地控制行为。
原文摘要 · Abstract (English)
In reinforcement learning, if the agent's reward differs from the designers' true utility, even only rarely, the state distribution resulting from the agent's policy can be very bad, in theory and in practice. When RL policies would devolve into undesired behavior, a common countermeasure is KL regularization to a trusted policy ("Don't do anything I wouldn't do"). All current cutting-edge language models are RL agents that are KL-regularized to a "base policy" that is purely predictive. Unfortunately, we demonstrate that when this base policy is a Bayesian predictive model of a trusted policy, the KL constraint is no longer reliable for controlling the behavior of an advanced RL agent. We demonstrate this theoretically using algorithmic information theory, and while systems today are too weak to exhibit this theorized failure precisely, we RL-finetune a language model and find evidence that our formal results are plausibly relevant in practice. We also propose a theoretical alternative that avoids this problem by replacing the "Don't do anything I wouldn't do" principle with "Don't do anything I mightn't do".
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