提升大模型指令遵循能力，防止奖励欺骗。
IFDECORATOR: Wrapping Instruction Following Reinforcement Learning with Verifiable Rewards
- 通过协同对抗数据飞轮生成更难的指令验证对。
- 87.43%准确率超越GPT-4o等更大模型。
- 用陷阱指令检测奖励滥用，适合安全强化学习研究者。
基于可验证奖励的强化学习（RLVR）虽能提升大语言模型（LLM）的指令遵循能力，但存在训练效率低和过优化问题——模型可能利用验证捷径而非真正理解用户意图。本文提出指令遵循装饰器（IFDecorator），构建一个鲁棒且样本高效的训练流程。其包含三个组件：（1）协同对抗数据飞轮，共同演化指令与混合验证，生成逐步增强的指令-验证对；（2）IntentCheck模块，强制模型对齐真实意图；（3）陷阱机制（trip wires），通过诱骗指令触发并捕获奖励黑客行为。Qwen2.5-32B-Instruct-IFDecorator在IFEval上达到87.43%准确率，优于更大的专有模型如GPT-4o。同时在FollowBench上显著提升，且保持通用能力。陷阱机制有效降低奖励欺骗率。代码、模型与数据将公开供后续研究使用。
原文摘要 · Abstract (English)
Reinforcement Learning with Verifiable Rewards (RLVR) improves instruction following capabilities of large language models (LLMs), but suffers from training inefficiency due to inadequate difficulty assessment. Moreover, RLVR is prone to over-optimization, where LLMs exploit verification shortcuts without aligning to the actual intent of user instructions. We introduce Instruction Following Decorator (IFDecorator}, a framework that wraps RLVR training into a robust and sample-efficient pipeline. It consists of three components: (1) a cooperative-adversarial data flywheel that co-evolves instructions and hybrid verifications, generating progressively more challenging instruction-verification pairs; (2) IntentCheck, a bypass module enforcing intent alignment; and (3) trip wires, a diagnostic mechanism that detects reward hacking via trap instructions, which trigger and capture shortcut exploitation behaviors. Our Qwen2.5-32B-Instruct-IFDecorator achieves 87.43% accuracy on IFEval, outperforming larger proprietary models such as GPT-4o. Additionally, we demonstrate substantial improvements on FollowBench while preserving general capabilities. Our trip wires show significant reductions in reward hacking rates. We will release models, code, and data for future research.
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