通过技能分类体系生成复杂组合数据，提升大模型的组合泛化能力
Towards Compositional Generalization of LLMs via Skill Taxonomy Guided Data Synthesis
- 基于结构信息论构建分层技能分类体系，揭示技能间潜在关系
- 设计熵最大化数据合成机制，在保持语义一致前提下生成高信息量组合
- 在指令跟随与代理任务中显著提升复杂组合泛化性能，适用于智能体系统
大语言模型与基于智能体的系统常因复杂技能组合呈长尾幂律分布而难以实现组合泛化，导致指令遵循与代理任务中的表现受限。为此，我们提出 STEPS：一种基于技能分类体系的熵驱动后训练数据合成框架，以生成具有挑战性的组合数据。STEPS 利用结构信息论，显式挖掘技能间的潜在关联，并构建可解释的分层技能分类体系。在此基础上，将数据合成建模为受约束的信息最大化问题，选择在分类层级中具有最大边际结构信息且保持语义连贯的技能组合。在多个挑战性指令遵循基准测试中，STEPS 显著优于现有数据合成基线，并在下游代理评估中实现了更优的组合泛化能力。
原文摘要 · Abstract (English)
Large Language Models (LLMs) and agent-based systems often struggle with compositional generalization due to a data bottleneck in which complex skill combinations follow a long-tailed, power-law distribution, limiting both instruction-following performance and generalization in agent-centric tasks. To address this challenge, we propose STEPS, a Skill Taxonomy guided Entropy-based Post-training data Synthesis framework for generating compositionally challenging data. STEPS explicitly targets compositional generalization by uncovering latent relationships among skills and organizing them into an interpretable, hierarchical skill taxonomy using structural information theory. Building on this taxonomy, we formulate data synthesis as a constrained information maximization problem, selecting skill combinations that maximize marginal structural information within the hierarchy while preserving semantic coherence. Experiments on challenging instruction-following benchmarks show that STEPS outperforms existing data synthesis baselines, while also yielding improved compositional generalization in downstream agent-based evaluations.
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