@inproceedings{tu-etal-2026-dynamic,
title = "Dynamic Dual-Granularity Skill Bank for Agentic {RL}",
author = "Tu, Songjun and
Xu, Chengdong and
Zhang, Qichao and
Zhang, Yaocheng and
Lan, Xiangyuan and
Li, Linjing and
Li, Dong and
Zhao, Dongbin",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.491/",
pages = "11294--11315",
ISBN = "979-8-89176-459-0",
abstract = "Agentic RL can benefit substantially from reusable experience, but existing skill-based methods mostly focus on trajectory-level guidance and provide limited support for the principled evaluation and maintenance of an evolving skill memory. We propose D2Skill, a dynamic dual-granularity skill bank for agentic RL that organizes reusable experience into task skills for high-level guidance and step skills for fine-grained decision support and error correction. D2Skill jointly trains the policy and skill bank through paired baseline and skill-injected rollouts under the same policy, using their performance gap to derive hindsight utility signals for both skill updating and policy optimization. Constructed from training-time trajectories, the skill bank is continuously expanded through reflection and maintained with utility-aware retrieval and pruning. Across ALFWorld, WebShop, and Search-Augmented QA, D2Skill consistently outperforms skill-free and skill-based baselines, remains effective with self-feedback, and transfers to unseen ALFWorld environments without test-time adaptation. Project Page: https://github.com/TU2021/D2Skill-AgenticRL"
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<abstract>Agentic RL can benefit substantially from reusable experience, but existing skill-based methods mostly focus on trajectory-level guidance and provide limited support for the principled evaluation and maintenance of an evolving skill memory. We propose D2Skill, a dynamic dual-granularity skill bank for agentic RL that organizes reusable experience into task skills for high-level guidance and step skills for fine-grained decision support and error correction. D2Skill jointly trains the policy and skill bank through paired baseline and skill-injected rollouts under the same policy, using their performance gap to derive hindsight utility signals for both skill updating and policy optimization. Constructed from training-time trajectories, the skill bank is continuously expanded through reflection and maintained with utility-aware retrieval and pruning. Across ALFWorld, WebShop, and Search-Augmented QA, D2Skill consistently outperforms skill-free and skill-based baselines, remains effective with self-feedback, and transfers to unseen ALFWorld environments without test-time adaptation. Project Page: https://github.com/TU2021/D2Skill-AgenticRL</abstract>
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%0 Conference Proceedings
%T Dynamic Dual-Granularity Skill Bank for Agentic RL
%A Tu, Songjun
%A Xu, Chengdong
%A Zhang, Qichao
%A Zhang, Yaocheng
%A Lan, Xiangyuan
%A Li, Linjing
%A Li, Dong
%A Zhao, Dongbin
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F tu-etal-2026-dynamic
%X Agentic RL can benefit substantially from reusable experience, but existing skill-based methods mostly focus on trajectory-level guidance and provide limited support for the principled evaluation and maintenance of an evolving skill memory. We propose D2Skill, a dynamic dual-granularity skill bank for agentic RL that organizes reusable experience into task skills for high-level guidance and step skills for fine-grained decision support and error correction. D2Skill jointly trains the policy and skill bank through paired baseline and skill-injected rollouts under the same policy, using their performance gap to derive hindsight utility signals for both skill updating and policy optimization. Constructed from training-time trajectories, the skill bank is continuously expanded through reflection and maintained with utility-aware retrieval and pruning. Across ALFWorld, WebShop, and Search-Augmented QA, D2Skill consistently outperforms skill-free and skill-based baselines, remains effective with self-feedback, and transfers to unseen ALFWorld environments without test-time adaptation. Project Page: https://github.com/TU2021/D2Skill-AgenticRL
%U https://aclanthology.org/2026.emnlp-main.491/
%P 11294-11315
Markdown (Informal)
[Dynamic Dual-Granularity Skill Bank for Agentic RL](https://aclanthology.org/2026.emnlp-main.491/) (Tu et al., EMNLP 2026)
ACL
- Songjun Tu, Chengdong Xu, Qichao Zhang, Yaocheng Zhang, Xiangyuan Lan, Linjing Li, Dong Li, and Dongbin Zhao. 2026. Dynamic Dual-Granularity Skill Bank for Agentic RL. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 11294–11315, Budapest, Hungary. Association for Computational Linguistics.