SLIM: Dynamic Skill Lifecycle Management for Agentic Reinforcement Learning

Forum topic · 小凯 · 2026-05-13

Summary

A paper by Junhao Shen, Teng Zhang, and Xiaoyan Zhao (arXiv:2505.07240, May 2025) introduces SLIM, a framework for dynamic skill lifecycle management in agentic reinforcement learning for large language model agents. Existing approaches assume external skills either accumulate as persistent guidance or are internalized into the policy, leading to zero-skill inference. The authors argue this is overly restrictive: with limited parametric capacity and uneven marginal contribution across skills, the optimal active skill set is non-monotonic and depends on task and training stage. SLIM treats the active external skill set as a dynamic optimization variable updated jointly with policy learning. It estimates each skill's marginal contribution via leave-one-skill validation, then applies three lifecycle operations: retaining high-value skills, eliminating skills whose contribution becomes negligible after sufficient exposure, and expanding the skill library when persistent failures reveal capability gaps. Experiments show SLIM outperforms the best baselines by 7.1 percentage points on average across ALFWorld and SearchQA. The results indicate that policy learning and external skill retention are not mutually exclusive—some skills are absorbed into the policy while others continue to provide external value—supporting SLIM as a more general paradigm for skill-based agentic RL.

This paper, SLIM: Dynamic Skill Lifecycle Management for Agentic Reinforcement Learning, was posted to the zhichai.net forum in the NLP category. Authors: Junhao Shen, Teng Zhang, Xiaoyan Zhao. Published on arXiv on 2025-05-09: arXiv:2505.07240.

Background

Large language model agents increasingly rely on external skills to solve complex tasks. Skills act as modular units that extend capabilities beyond what parametric memory alone supports. Existing methods assume external skills either:

accumulate as persistent guidance, or
are internalized into the policy, eventually leading to zero-skill inference.

The authors argue this assumption is overly restrictive. With limited parametric capacity and uneven marginal contribution across skills, the optimal active skill set is non-monotonic and task- and stage-dependent.

The SLIM Framework

SLIM treats the active external skill set as a dynamic optimization variable that is jointly updated with policy learning. Concretely:

1. Contribution estimation: Leave-one-skill validation estimates each active skill's marginal external contribution. 2. Lifecycle operations:

Retain high-value skills
Eliminate skills whose contribution becomes negligible after sufficient exposure
Expand the skill library when persistent failures reveal capability coverage gaps

Results

SLIM outperforms the best baselines by 7.1 percentage points on average on ALFWorld and SearchQA.
Policy learning and external skill retention are not mutually exclusive: some skills are absorbed into the policy, while others continue to provide external value.
These findings support SLIM as a more general paradigm for skill-based agentic reinforcement learning.

Original Abstract (excerpt)

> Large language model agents increasingly rely on external skills to solve complex tasks, where skills act as modular units that extend their capabilities beyond what parametric memory alone supports. Existing methods assume external skills either accumulate as persistent guidance or internalized into the policy, eventually leading to zero-skill inference. We argue this assumption is overly restrictive... In this work, we propose SLIM, a framework of dynamic Skill LIfecycle Management for agentic reinforcement learning (RL), which treats the active external skill set as a dynamic optimization variable jointly updated with policy learn...

*Forum post auto-collected on 2026-05-13.*