@inproceedings{huang-etal-2026-ability,
title = "Ability-Aligned Skill Evolution for {LLM} Agents via Reinforcement Learning",
author = "Huang, Fei and
Chen, Tianyu and
He, Zhongyu and
Li, Yuanfan and
Yu, Meng Hsuan and
Li, Xingyang and
Pan, Lu and
Zeng, Ke and
Cai, Xunliang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.136/",
pages = "2779--2794",
ISBN = "979-8-89176-462-0",
abstract = "Large Language Model agents augmented with external skill banks have demonstrated remarkable performance on sequential decision-making tasks. However, existing methods update skills in an undirected, unverified manner and prematurely penalize agents for failing to utilize immature skills. We introduce a structured cognitive representation that decomposes both general and task-specific skills into four dimensions{---}D-PLAN, D-TRACK, D-EXEC, and D-GUARD{---}enabling precise failure attribution. Building upon this representation, we propose A$^2$SE, an ability-aligned skill evolution framework that synchronizes skill bank updates with the agent{'}s learning trajectory. Specifically, A$^2$SE concentrates evolution on the identified bottleneck dimension, safeguards updates via a causal verification mechanism with per-task-type rollback, and employs a competence-aligned skill reward to alleviate premature penalization. Extensive experiments on ALFWorld and WebShop demonstrate that A$^2$SE achieves 11.0{\%} and 7.1{\%} improvements over the strongest baselines, respectively, with ablation studies confirming the necessity of each proposed component."
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<abstract>Large Language Model agents augmented with external skill banks have demonstrated remarkable performance on sequential decision-making tasks. However, existing methods update skills in an undirected, unverified manner and prematurely penalize agents for failing to utilize immature skills. We introduce a structured cognitive representation that decomposes both general and task-specific skills into four dimensions—D-PLAN, D-TRACK, D-EXEC, and D-GUARD—enabling precise failure attribution. Building upon this representation, we propose A²SE, an ability-aligned skill evolution framework that synchronizes skill bank updates with the agent’s learning trajectory. Specifically, A²SE concentrates evolution on the identified bottleneck dimension, safeguards updates via a causal verification mechanism with per-task-type rollback, and employs a competence-aligned skill reward to alleviate premature penalization. Extensive experiments on ALFWorld and WebShop demonstrate that A²SE achieves 11.0% and 7.1% improvements over the strongest baselines, respectively, with ablation studies confirming the necessity of each proposed component.</abstract>
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%0 Conference Proceedings
%T Ability-Aligned Skill Evolution for LLM Agents via Reinforcement Learning
%A Huang, Fei
%A Chen, Tianyu
%A He, Zhongyu
%A Li, Yuanfan
%A Yu, Meng Hsuan
%A Li, Xingyang
%A Pan, Lu
%A Zeng, Ke
%A Cai, Xunliang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F huang-etal-2026-ability
%X Large Language Model agents augmented with external skill banks have demonstrated remarkable performance on sequential decision-making tasks. However, existing methods update skills in an undirected, unverified manner and prematurely penalize agents for failing to utilize immature skills. We introduce a structured cognitive representation that decomposes both general and task-specific skills into four dimensions—D-PLAN, D-TRACK, D-EXEC, and D-GUARD—enabling precise failure attribution. Building upon this representation, we propose A²SE, an ability-aligned skill evolution framework that synchronizes skill bank updates with the agent’s learning trajectory. Specifically, A²SE concentrates evolution on the identified bottleneck dimension, safeguards updates via a causal verification mechanism with per-task-type rollback, and employs a competence-aligned skill reward to alleviate premature penalization. Extensive experiments on ALFWorld and WebShop demonstrate that A²SE achieves 11.0% and 7.1% improvements over the strongest baselines, respectively, with ablation studies confirming the necessity of each proposed component.
%U https://aclanthology.org/2026.findings-emnlp.136/
%P 2779-2794
Markdown (Informal)
[Ability-Aligned Skill Evolution for LLM Agents via Reinforcement Learning](https://aclanthology.org/2026.findings-emnlp.136/) (Huang et al., Findings 2026)
ACL
- Fei Huang, Tianyu Chen, Zhongyu He, Yuanfan Li, Meng Hsuan Yu, Xingyang Li, Lu Pan, Ke Zeng, and Xunliang Cai. 2026. Ability-Aligned Skill Evolution for LLM Agents via Reinforcement Learning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 2779–2794, Budapest, Hungary. Association for Computational Linguistics.