Dynamic Skill Lifecycle Management for Agentic Reinforcement Learning

Problem
This paper addresses a gap in the literature regarding the management of external skills in reinforcement learning (RL) agents, particularly in the context of large language model agents. Existing frameworks typically assume that external skills either accumulate as persistent guidance or are fully internalized into the policy, leading to a scenario where agents may eventually operate without any external skills (zero-skill inference). The authors argue that this assumption is overly simplistic, as the optimal set of active skills is non-monotonic and varies depending on the task and the stage of learning. This work is presented as a preprint and has not yet undergone peer review.

Method
The authors introduce SLIM (Skill Lifecycle Management), a framework that dynamically manages the active external skill set in conjunction with policy learning. SLIM employs a leave-one-skill-out validation approach to estimate the marginal contribution of each active skill. The framework incorporates three key lifecycle operations: (1) retaining high-value skills that significantly contribute to performance, (2) retiring skills that become redundant after sufficient exposure, and (3) expanding the skill bank when persistent failures indicate gaps in capability coverage. The architecture is designed to optimize the skill set dynamically, allowing for a more nuanced integration of external skills into the RL process.

Results
SLIM demonstrates a significant performance improvement over baseline methods, achieving an average increase of 7.1 percentage points across two benchmarks: ALFWorld and SearchQA. The results indicate that SLIM not only enhances the overall performance of the agents but also reveals that policy learning and external skill retention can coexist effectively. Some skills are absorbed into the policy, while others continue to provide external value, suggesting that SLIM offers a more flexible and effective paradigm for skill-based agentic RL.

Limitations
The authors acknowledge that their framework may require careful tuning of the skill lifecycle operations to optimize performance across diverse tasks. Additionally, the reliance on leave-one-skill-out validation may introduce computational overhead, particularly in environments with a large skill bank. The paper does not address the scalability of SLIM in highly dynamic environments where the skill set may need to adapt rapidly. Furthermore, the long-term implications of skill retention and retirement on agent performance remain unexplored.

Why it matters
The implications of this work are significant for the development of more capable and adaptable RL agents. By treating the active skill set as a dynamic optimization variable, SLIM provides a framework that can enhance the efficiency and effectiveness of skill utilization in RL. This approach could lead to more robust agents capable of tackling complex tasks that require a diverse range of skills, ultimately advancing the state of agentic reinforcement learning. The findings encourage further exploration into dynamic skill management strategies, potentially influencing future research on modular skill integration in AI systems.

Authors: Junhao Shen, Teng Zhang, Xiaoyan Zhao, Hong Cheng
Source: arXiv:2605.10923
URL: https://arxiv.org/abs/2605.10923v1