RADEG: Reward-Aware Dynamic Execution Gate for Skill-Based LLM Agents
A recent submission to arXiv presents RADEG (Reward-Aware Dynamic Execution Gate), a streamlined decision-making layer for large language model (LLM) agents utilizing skill libraries. Cataloged as arXiv:2608.09168v1, this research tackles the issue of whether a retrieved skill bundle should be executed, considering the significant computational expense associated with each skill-conditioned rollout. RADEG operates independently of the retriever, positioned between skill retrieval and agent execution. It develops a cost-effective surrogate model to estimate the utility of a query-bundle pair prior to initiating the costly rollout. To enhance supervision while managing task complexity, the method modifies each retrieved bundle by adding, removing, or substituting a skill. The abstract emphasizes the growing necessity of this decision-making process as skill libraries expand, aiming to enhance efficiency by avoiding unproductive executions.
Key facts
- Paper arXiv:2608.09168v1 introduces RADEG, a Reward-Aware Dynamic Execution Gate.
- RADEG is a lightweight, retriever-agnostic decision layer between skill retrieval and agent execution.
- It predicts execution utility of a query-bundle pair before launching expensive rollout.
- The method locally perturbs retrieved bundles by deleting, adding, or replacing one skill.
- The paper addresses the challenge of deciding whether a retrieved skill bundle should be executed.
- Skill-conditioned rollouts are computationally expensive.
- The approach aims to improve efficiency by skipping unnecessary executions.
- The paper is announced as a new submission on arXiv.
Entities
Institutions
- arXiv