让大模型同时用文字和参数生成技能，实现更优的自主解决问题能力。
SkillSmith: Learning to Compose Parametric Skills and Textual Knowledge
- 用前缀调优让大模型直接推理模型参数，融合文本与权重信息
- 在多个任务上超越纯文本或纯参数方法，性能显著提升
- 适合需要多模态知识融合的智能体系统开发
由大语言模型驱动的智能体系统通常依赖两个核心机制：从过往经验中合成基于文本的知识与流程，以及为重复出现的子目标构建参数化（权空间）技能库。然而，现有研究大多将两者视为独立任务：要么通过组合与反思组织文本知识，要么通过权空间合并积累参数技能。因此，如何无缝整合文本与模型权重以实现针对性性能提升仍鲜有探索。本文通过将模型权重视为大模型可原生推理的额外模态，弥合这一鸿沟。我们采用前缀调优实现参数学习，并增强大模型以同时处理前缀权重与富含目标能力关联的丰富文本数据。由此构建的增强型大模型——SkillSmith，能融合两类输入，执行指令引导的参数合成，直接输出体现目标技能的新前缀权重。实验表明，该方法显著优于仅依赖文本或仅依赖权重的基线，实现了单模态（纯文本或纯参数）适配无法达到的性能增益。
原文摘要 · Abstract (English)
Agentic systems driven by large language models (LLMs) regularly feature two key mechanisms to autonomously solve complex problems: synthesizing text-based knowledge and procedures from past experiences and building parametric (weight-space) skill libraries for recurring sub-goals. To date, research has largely treated these as orthogonal pursuits: either organizing textual knowledge through composition and reflection, or consolidating parametric skills via weight-space merging. Consequently, the seamless integration of text and model weights for targeted performance improvements remains largely unexplored. This work bridges this modality gap by treating model weights as an additional modality that an LLM can natively reason over. We instantiate parametric learning via prefix-tuning and augment an LLM to ingest both prefix weights and rich textual data which capture relationships to a target capability. Our augmented LLM, which we call SkillSmith, synthesizes these inputs to perform instruction-steered parametric synthesis, directly outputting new prefix weights that manifest the target skill. We demonstrate that our approach significantly outperforms both text-only and weight-space-only baselines, unlocking performance gains that are out of reach for uni-modal (text-only or weight-only) adaptations.
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