AtomSD: Procedural Atom-Guided Self-Distillation for LLM Agents
Large language models (LLMs) possess broad factual knowledge and strong reasoning capabilities, but solving complex tasks also requires procedural knowledge about how to act. Existing approaches often represent procedural knowledge through trajectories or bundled skills, limiting flexible reuse across interaction histories. This paper introduces Procedural Atoms, fine-grained and independently applicable units that represent Intent, When, Do, and Avoid guidance. Building on this representation, we propose AtomSD, an Atom-guided Self-Distillation framework in which the learner continually extracts Procedural Atoms from its own successful trajectories and distills them into the evolving pol...
| Authors | Bohan Yu, Pengfei Cao, Kai Tian et al. |
|---|---|
| Primary category | cs.CL |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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