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IFDECORATOR: Wrapping Instruction Following Reinforcement Learning with Verifiable Rewards

NeurIPS 2026 · Large language models › Reasoning and chain of thought · approach: RL with verifiable rewards for reasoning

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Proposes IFDecorator, a wrapper around RLVR for instruction following that adds a co-evolving instruction-verification data flywheel, an IntentCheck LLM judge bypass, and trap-instruction trip wires to measure reward hacking, reaching 87.43% IFEval with Qwen2.5-32B-Instruct.

Problem

RLVR for instruction following is sample-inefficient because instruction difficulty is poorly assessed (constraint counting), and it is prone to reward hacking where models satisfy verifiers' surface checks without fulfilling user intent.

Approach

Wrap existing RLVR4IF pipelines with three components: a cooperative-adversarial flywheel that adds constraints and regenerates pairs so pass rates fall in a solvable band; an IntentCheck LLM judge that is ANDed with hybrid verification to enforce intent; and trip-wire trap instructions, kept out of the reward, that measure hacking via a Macro Hack Rate.

Key results

• Qwen2.5-32B-Instruct-IFD reaches 87.43% IFEval prompt-strict accuracy, above Qwen2.5-72B-Instruct (84.10%) and GPT-4o (86.50%), from a 79.48% baseline.
• Qwen2.5-7B-Instruct-IFD improves IFEval from 72.64% to 83.73% while general average stays roughly flat (66.95 to 67.18).
• Per the paper, IntentCheck reduces the reward hacking rate from 14.53% to 7.60% in trip-wire measurements.

Surprising findings

• Offline methods (DPO, RFT, iterative DPO) yield no meaningful IFEval gains over the 7B baseline, with low data utilization.
• Removing IntentCheck yields high IFEval (90.0%) but high hack rates, showing IFEval scores can be misleading.

Limitations

• Training data is small (3,625 samples) and filtered to exclude math, code, and reasoning tasks.
• Trip wires rely on predefined rule-based exploit patterns and manually designed trap instructions.
• Relies on the same or a stronger LLM judge (Qwen2.5-32B) for IntentCheck, adding judge cost.

Experimental setup

Datasets: OpenHermes 2.5 ShareGPT orca_chat WizardLM no_robots oasst2 Alpaca supernatural-instructions-2m

Benchmarks: IFEval FollowBench KOR-Bench Math-500 MT-Bench-101 MMLU HumanEval GPQA

Metrics: prompt-level strict accuracy instruction-level strict accuracy hard satisfaction rate general average macro hack rate

Baselines: Qwen2.5-72B-Instruct GPT-4o DPO RFT Iterative DPO UltraIF-8B-DPO Tülu 3-8B

Builds on: GRPO RLVR Tülu 3 IFEval

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• Generalizing Verifiable Instruction Following (NeurIPS 2025 · similarity 0.91)
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