策略优化偏好阻力最小的路径
Policy Optimization Prefers The Path of Least Resistance
AI总结：
本研究通过受控实验发现，策略优化在开放式思维链下会退化为直接回答格式，即使高奖励权重也无法阻止，揭示其优先优化简单奖励组件的原理，对奖励黑客构成挑战。
摘要：
策略优化（PO）算法被用于改进大型语言模型，以完成复杂的多步推理任务。当前最先进的流水线强制采用严格的“先思考后回答”格式来引出思维链（CoT）；然而，当这些刚性约束被放宽为开放式CoT结构时，PO的行为仍是一个研究不足的问题。我们通过一系列受控实验来研究这一空白，并识别出一个一致的原理：\textit{策略优化始终遵循阻力最小的路径}。当被赋予交错进行推理和响应的灵活性时，策略优化始终学会丢弃显式推理，导致策略退化为直接的\texttt{<answer>}仅格式。这一结果在多种模型和算法中均成立。我们发现，即使将复杂的\texttt{<think><answer>}格式分配高达4倍的奖励权重，这种格式崩溃仍然持续存在。我们通过一系列受控的奖励分解实验将这一原理形式化，展示了一个清晰的层级：PO系统性地优先优化最简单的奖励组件，即使面对互斥的选择或对更复杂行为的强烈激励，这种偏好依然成立。最后，我们表明，成功收敛于高奖励捷径并非低努力漂移，而是由优化过程驱动，该过程要求KL正则化策略具有足够的自由度，以从其初始先验进行显著偏移。我们的发现揭示，赋予策略偏离的自由是一把双刃剑：虽然这对于发现高奖励捷径是必要的，但它也创造了强大的激励去博弈奖励函数中最简单的方面，对对齐下的奖励黑客行为构成了关键挑战。
英文摘要：
Policy optimization (PO) algorithms are used to refine Large Language Models for complex, multi-step reasoning. Current state-of-the-art pipelines enforce a strict think-then-answer format to elicit chain-of-thought (CoT); however, the behavior of PO when these rigid constraints are relaxed into an open-ended CoT structure remains an under-studied question. We investigate this gap with an extensive suite of controlled experiments and identify a consistent principle: \textit{policy optimization consistently follows the path of least resistance}. When afforded the flexibility to interleave reasoning and response, policy optimization consistently learns to discard explicit reasoning, causing the policy to degenerate to a direct \texttt{<answer>}-only format. This outcome holds true across various models and algorithms. We find that this collapse in format is persistent even when the complex \texttt{<think><answer>} format is assigned up to 4x larger reward weights. We formalize this principle through a series of controlled reward decomposition experiments, demonstrating a clear hierarchy: PO systematically optimizes for the simplest reward component first, a preference that holds even when faced with mutually exclusive choices or strong incentives for more complex behaviors. Finally, we show that successful convergence on the high-reward shortcut is not a low-effort drift but is driven by the optimization process that requires the KL-regularized policy to have sufficient freedom to make a significant shift from its initial prior. Our findings reveal that granting policies the freedom to diverge is a double-edged sword: while necessary for discovering high-reward shortcuts, it also creates a powerful incentive to game the simplest aspects of the reward function, posing a critical challenge for reward hacking under alignment.
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