[Submitted on 17 Oct 2026]
Title:AtomSD: Procedural Atom-Guided Self-Distillation for LLM Agents
View a PDF of the paper titled AtomSD: Procedural Atom-Guided Self-Distillation for LLM Agents, by Bohan Yu and 10 other authors
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Abstract: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 policy through Atom-conditioned supervision and task-reward optimization. Comprehensive experiments show that AtomSD consistently outperforms strong reward-based and distillation methods, exceeding the state-of-the-art baseline by up to 9.31 points in average performance. Further analyses demonstrate that the proposed Procedural Atoms provide effective guidance, highlighting the value of atom-level procedural knowledge for policy learning.
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2610.26926 [cs.CL]
(or arXiv:2610.26926v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.26926
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Bohan Yu [view email] [v1] Sat, 17 Oct 2026 14:12:03 UTC (3,380 KB)
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