arXiv AI By Huaiyuan Yao, Xiaoou Liu, Charles Fleming, Tianlong Chen, Hua Wei

MASkills: Continual Skills Optimization for Multi-Agent LLM Systems

Read the original on arXiv AI →

MASkills is a continual learning framework designed to enhance multi‑agent large language model (LLM) systems by optimizing their agent skills. It introduces a new agent‑optimization pipeline that combines skill‑conditioned credit assignment, hierarchical credit aggregation, and momentum‑smoothed optimization, allowing skill libraries to evolve through refinement, induction, consolidation, and pruning. Experiments on HotpotQA, LoCoMo, and GAIA demonstrate its effectiveness across multiple agentic tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
3d ago

CODESKILL: Learning Self-Evolving Skills for Coding Agents

CODESKILL is an LLM-based framework that learns to extract, evolve, and maintain procedural skills from coding-agent trajectories. It treats skill extraction and skill-bank management as a learnable policy trained with reinforcement learning, using a hybrid reward combining rubric-based skill quality and verifiable execution feedback. Experiments on EnvBench, SWE-Bench Verified, and Terminal-Bench 2 demonstrate that CODESKILL raises average pass rates by 11.03 over a no-skill baseline and by 5.10 over the strongest prompt-based or memory baseline while keeping a compact skill bank.

By Yanzhou Li, Yiran Zhang, Xiaoyu Zhang, Xiaoxia Liu, Yang Liu
arXiv AI
Sep 23

ACLArena: Agent Continue Learning in Multi-stage Post-training

arXiv:2609.23989v1 Announce Type: new Abstract: Building general-purpose agents for industrial deployment requires integrating multiple capabilities, each typically acquired at a distinct stage of tr...

By Haixin Wang, Xiaoxuan Wang, Junkai Zhang, Han Zhang, Renliang Sun, Alexander K Taylor, Yidan Shi, Haoran Deng, Chenguang Wang, Jason Cong, Yizhou Sun, Wei Wang