The paper introduces BiPA, a method for evaluating skill optimizers that rewrite natural‑language procedures for agents. By replaying the optimizer’s acceptance rule on identical candidates and recording episodes as bipartite graphs of turns and read text, BiPA isolates the effect of selection from the cost of added input tokens. Experiments on three optimizers, three benchmarks, and two models show that learned skills perform only marginally better than hand‑written seeds, and that apparent dev‑set gains often disappear on test, revealing that optimizers tend to add redundant rules that increase token usage.
By Jiawei Zhou, Bo Peng, Yuxuan Huang, Ming Zhang, Youbang Sun, Xiang Wang, Chaochao Lu
arXiv:2610.35434v1 Announce Type: new
Abstract: Recursive self-improvement (RSI), in which a system also improves the way it improves, is a long-standing aspiration on the path to AGI. Language agent...
By Wangcheng Tao, Zhanglu Yan, Han Wu, Weng-Fai Wong
SkillGate is a method that trains agents to select the correct skill from a large slate during an episode by separating credit signals for skill selection and execution. It addresses the problem of selector credit starvation, where traditional outcome-rewarded RL fails to give sufficient credit to the skill-naming tokens, especially in long-horizon tasks. Experiments on five benchmarks show that SkillGate improves a 9B policy’s success rate from 40.8% to 53.2%, reduces exposure to misleading candidates, and requires fewer skill reads.
By Qingyao Li, Wenxiang Jiao, Shuai Shao, Kangning Zhang, Yuan Lu, Yi Guo, Weiwen Liu, Weinan Zhang, Yong Yu
The paper introduces VERSE, a Verified Self‑Evolving optimizer that enhances LLM agent harnesses by allowing the optimizer to test edits, replay failures, and perturb steps while tracking fixes and regressions. VERSE builds its own tools for failure analysis, verification, training audits, and workflow control, and uses this feedback to revise the harness’s prompts, skills, tools, hooks, and notes without changing model weights. In experiments across five executors and multiple languages, VERSE improves all evaluated harness optimizers, achieving higher accuracy on held‑out and out‑of‑distribution tasks compared to the strongest baselines.
By Zekai Wang, Yingqiang Ge, Zekun Wang, Hai Wang, Yuhui Xu, Joshua Frandsen, Shancong Fu, Ashia C. Wilson, Chandan K. Reddy
EvoSkill v2 is a three‑agent system that edits the instructions (skills) given to an agent while keeping its model and harness unchanged. The system comprises a benchmark adaptor that turns user‑supplied benchmarks and graders into plugins, a solver that attempts tasks, and a stateful evolver that rewrites skills each epoch based on the solver’s training rollouts. Compared to existing skill optimizers, EvoSkill v2’s evolver uses a single coding agent with a bash tool, accepts every epoch’s skills, and allows multiple attempts per training task, achieving higher held‑out pass rates and lower cost per performance gain on SpreadsheetBench v2 and $ au^3$‑bench banking.
whyItMatters":"EvoSkill v2 demonstrates that a single, stateful evolver can more efficiently improve agent skills, enabling even less capable models to achieve significant performance gains in benchmark tasks."
By Dastin Huang, Baran Nama, Salaheddin Alzubi
SkillNEO introduces a non‑elitist evolutionary framework for refining agent skills, allowing lower‑reward versions to receive further edits and preserve useful partial repairs. By separating the selection of a version to use from the selection of versions to improve, the method identifies which steps to keep and which failures to fix, even revisiting earlier versions with new evidence. Experiments on 86 SkillsBench tasks across eight domains show significant reward gains for GPT‑5.4 Mini and substantial improvements when the evolved skills are reused by stronger models such as GPT‑5.5 and GPT‑5.6 Luna.
By Enze Ma, Chunyu Miao, Yankai Chen, Jie Yang, Liancheng Fang, Kening Zheng, Hanrong Zhang, Muyao Xu, Xinle Yu, Chengze Li, Wei-Chieh Huang, Ziwei Ren, Yunhao Du, Zhixiang Shen, Xue Liu, Philip S. Yu, Zhen Wang