HAIR: Hardness-Aware Inverse Reinforcement Learning with Introspective Reasoning for LLM Alignment

The alignment of large language models (LLMs) with human values remains critical yet hindered by four key challenges: (1) scarcity of balanced safety datasets, (2) alignment tax, (3) vulnerability to jailbreak attacks due to shallow alignment, and (4) inability to dynamically adapt rewards according to task difficulty. To address these limitations, we introduce HAIR (Hardness-Aware Inverse Reinforcement Learning with Introspective Reasoning), a novel alignment approach inspired by shadow models in membership inference attacks. Our approach consists of two main components: (1) construction of a balanced safety Chain-of-Draft (CoD) dataset for seven harmful categories using structured prompts that leverage the introspective reasoning capabilities of LLMs; and (2) training of category-specific reward models with Group Relative Policy Optimization (GRPO), dynamically tuning optimization to task difficulty at both the data and model levels. Comprehensive experiments across four harmlessness and four usefulness benchmarks demonstrate that HAIR achieves state-of-the-art performance, outperforming all baseline methods in safety while maintaining high levels of usefulness.