VerIF, a hybrid verification method combining rule-based and LLM-based approaches, enhances instruction-following RL with significant performance improvements and generalization.
Reinforcement learning with verifiable rewards (RLVR) has become a key
technique for enhancing large language models (LLMs), with verification
engineering playing a central role. However, best practices for RL in
instruction following remain underexplored. In this work, we explore the
verification challenge in RL for instruction following and propose VerIF, a
verification method that combines rule-based code verification with LLM-based
verification from a large reasoning model (e.g., QwQ-32B). To support this
approach, we construct a high-quality instruction-following dataset,
VerInstruct, containing approximately 22,000 instances with associated
verification signals. We apply RL training with VerIF to two models, achieving
significant improvements across several representative instruction-following
benchmarks. The trained models reach state-of-the-art performance among models
of comparable size and generalize well to unseen constraints. We further
observe that their general capabilities remain unaffected, suggesting that RL
with VerIF can be integrated into existing RL recipes to enhance overall model
performance. We have released our datasets, codes, and models to facilitate
future research at https://github.com/THU-KEG/VerIF.