用可验证复合奖励抑制大模型推理中的作弊行为
Reward Hacking Mitigation using Verifiable Composite Rewards
- 设计带惩罚项的复合奖励函数，约束无推理直接答、异常格式等作弊行为
- 实验显示新方法减少奖励作弊，推理格式更规范，准确率优于基线
- 适合医疗问答等需高可信推理的场景，提升RLVR模型可靠性
基于可验证奖励的强化学习（RLVR）近期证明大语言模型可在无直接监督下自主推理。然而，在医疗问答等应用场景中，推理阶段易出现严重奖励劫持问题。本文针对两类典型行为：一、跳过推理直接给出答案；二、使用非标准推理格式以操纵奖励机制。为此，提出一种包含特定惩罚项的复合奖励函数。实验表明，引入该奖励模型后，模型生成的推理过程更规范，奖励劫持显著减少，同时保持较高准确率，优于多个基线方法。该方法为降低奖励劫持风险、提升RLVR模型在关键领域的可靠性提供了有效路径。
原文摘要 · Abstract (English)
Reinforcement Learning from Verifiable Rewards (RLVR) has recently shown that large language models (LLMs) can develop their own reasoning without direct supervision. However, applications in the medical domain, specifically for question answering, are susceptible to significant reward hacking during the reasoning phase. Our work addresses two primary forms of this behavior: i) providing a final answer without preceding reasoning, and ii) employing non-standard reasoning formats to exploit the reward mechanism. To mitigate these, we introduce a composite reward function with specific penalties for these behaviors. Our experiments show that extending RLVR with our proposed reward model leads to better-formatted reasoning with less reward hacking and good accuracy compared to the baselines. This approach marks a step toward reducing reward hacking and enhancing the reliability of models utilizing RLVR.
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