Abstract
Following complex instructions with multiple explicit constraints remains a fundamental challenge for large language models (LLMs). Existing alignment methods, such as DPO, optimize holistic reward signals that often underemphasize strict satisfaction of individual constraints, particularly under out-of-distribution or multi-constraint settings.
In this paper, we propose STAIF, a stage-wise optimization framework that decouples the alignment of subjective (soft) constraints from the optimization of objectively verifiable (hard) constraints.
Method Overview
- Stage 1: Applies preference optimization with multiple negative samples to sharpen sensitivity to soft constraints.
- Stage 2: Applies Reinforcement Learning with Verifiable Rewards (RLVR) to enforce strict compliance with hard constraints.
To support this method, we construct STAINSTRUCT, a high-quality bilingual (English, Chinese) dataset of approximately 31,000 complex multi-constraint instructions. Extensive analyses validate the design of STAIF and show state-of-the-art performance on representative benchmarks against strong baselines, as well as genuine generalization.
Blogger's Review: STAIF's stage-wise optimization approach offers a new perspective on executing complex instructions by separating the treatment of soft and hard constraints. This innovation not only enhances the model's flexibility but also improves the accuracy of instruction following in multi-constraint environments, marking a significant advancement in large language models' ability to handle complex tasks.