IFDECORATOR: Wrapping Instruction Following Reinforcement Learning with Verifiable Rewards

ICLR 2026 Conference Submission18895 Authors

Keywords: Large Language Model, Instruction Following, Reinforcement Learning

TL;DR: We present IFD, a training framework that balances instruction difficulty and enforces intent alignment. It exposes shortcut behaviors and significantly improves both robustness and accuracy.

Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) improves instruction following capabilities of large language models (LLMs), but suffers from 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 by injecting trap instructions to 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.

Primary Area: foundation or frontier models, including LLMs

Submission Number: 18895