提出新方法防止强化学习中因反馈循环导致的对齐崩溃
Explaining and Preventing Alignment Collapse in Iterative RLHF
- 用博弈论分析策略与奖励模型的互动，发现策略会误导奖励模型
- 标准迭代RLHF会因忽略策略影响导致输出质量下降
- 提出前瞻优化机制，有效防止对齐崩溃，适用于大模型对齐
强化学习从人类反馈（RLHF）通常假设奖励模型（RM）是静态或非策略性的。但在迭代部署中，策略生成用于重训练RM的数据，形成反馈环路。基于斯塔克尔伯格博弈建模这一交互，我们推导出策略真实优化梯度的解析分解：包含标准策略梯度和一个参数引导项，该术语捕捉策略对未来RM参数的影响。我们证明，标准迭代RLHF完全忽略此引导项，导致对齐崩溃：策略系统性地利用RM的盲点，生成低质量但高奖励的输出，其反馈进一步强化了自身错误。为缓解此问题，我们提出前瞻性策略优化（FPO），通过正则化策略对RM更新的参数引导效应，恢复缺失的引导项。我们以可扩展的一阶近似实现FPO，并在受控环境及使用Llama-3.2-1B的大语言模型对齐流水线中验证其能有效防止对齐崩溃。
原文摘要 · Abstract (English)
Reinforcement learning from human feedback (RLHF) typically assumes a static or non-strategic reward model (RM). In iterative deployment, however, the policy generates the data on which the RM is retrained, creating a feedback loop. Building on the Stackelberg game formulation of this interaction, we derive an analytical decomposition of the policy's true optimization gradient into a standard policy gradient and a parameter-steering term that captures the policy's influence on the RM's future parameters. We show that standard iterative RLHF, which drops this steering term entirely, suffers from alignment collapse: the policy systematically exploits the RM's blind spots, producing low-quality, high-reward outputs whose feedback reinforces the very errors it exploits. To mitigate this, we propose foresighted policy optimization (FPO), a mechanism-design intervention that restores the missing steering term by regularizing the policy's parameter-steering effect on RM updates. We instantiate FPO via a scalable first-order approximation and demonstrate that it prevents alignment collapse on both controlled environments and an LLM alignment pipeline using Llama-3.2-1B.
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