ReGE is a framework that transforms recorded interactive task experience into guidance for new attempts. Its implementation, ReGE‑Syn, synthesizes execution records and artifacts into concise, self‑contained instructions using a single coach call, without updating model parameters. Experiments on four benchmarks show that ReGE improves accuracy by roughly 9–10 percentage points for GPT and Claude, and significantly reduces guidance‑construction tokens while maintaining success rates.
By Jiarong Liang, Yubo Wang, Zijie Liu, Yuxuan Zhang, Ping Nie, Wenhu Chen
State-OPD introduces a state‑grounded on‑policy distillation framework that separates the objective task and action‑observation record from the student’s internal reasoning trajectory. By removing prior student reasoning and resampling complete reasoning‑action responses at high‑entropy states, the method improves teacher supervision and action diversity. Evaluations on ALFWorld, ScienceWorld, and WebShop show State‑OPD achieves the highest macro‑average success rates for Qwen3‑1.7B and Qwen3‑4B students, even surpassing a larger teacher model on certain tasks.
By Zhentao Zhang, Yanzhao Zheng, Yanlin Liu, Yuanqiang Yu, Tianze Xu, Chao Ma, Jihuai Zhu, Lelun Wu, Baohua Dong, Hangcheng Zhu, Ruohui Huang, Gang Yu
arXiv:2603. 25158v5 Announce Type: replace Abstract: Large Language Model (LLM) agents increasingly rely on domain-specific skills, yet manually authoring such skills does not scale, and skills generated purely from parametric knowledge often miss critical operational pitfalls.
By Jingwei Ni, Yihao Liu, Xinpeng Liu, Yutao Sun, Mengyu Zhou, Pengyu Cheng, Dexin Wang, Erchao Zhao, Xiaoxi Jiang, Guanjun Jiang
arXiv:2609.37304v1 Announce Type: new
Abstract: Large reasoning models improve performance on challenging problems by allocating additional computation before answering, but longer reasoning does not...
By Zhibin Wen, Tao Han, Lei Bai, Can Li, Yang Xu
arXiv:2607. 20440v1 Announce Type: cross Abstract: Proprietary large language models (LLMs) entail substantial intellectual and financial investment, making them valuable intellectual property (IP).
By Fan Li, Mengting Pan, Sijia Xu, Xiaoyang Wang, Chen Chen, Wenjie Zhang
The paper introduces the Agent-Editing World Model (AEWM), a new approach that models how reasoning and actions influence future task progress instead of simulating tool responses. AEWM includes an Action Judge that classifies decisions as Critical, Exploratory, or Noisy, and a State Revision mechanism that edits noisy reasoning–action continuations from the same observed history. The integrated system, EditAct, directly updates the underlying state during real execution, leading to significant performance gains across multiple benchmarks and agent backbones.
By Shuang Sun, Guoxin Chen, Fanzhe Meng, Jia Deng, Huatong Song, Jinhao Jiang, Wayne Xin Zhao, Hongteng Xu, Ji-Rong Wen
arXiv:2607. 24280v1 Announce Type: new Abstract: Agentic search enables large language models to solve knowledge-intensive tasks by interleaving multi-step reasoning with retrieval, yet optimizing this with outcome-based reinforcement learning (RL) provides only sparse supervision.
By Junlin Liu, Jiangwang Chen, Zixin Song, Shuaiyu Zhou, Chunji Lv, Hank Wu, Kailin Jiang, Jinyang Wu, Bohan Yu, Chenxi Zhou
GraphOPD introduces a graph‑augmented on‑policy distillation method for large language model agents, addressing the shortcomings of existing divergence‑based guidance when rewards are sparse. By constructing a dependency graph from the environment’s state‑change record and applying a random‑walk stationary distribution, the method identifies and prioritizes the most impactful steps in a trajectory, fusing this structural credit with teacher‑student divergence signals. Experiments on ALFWorld, WebShop, and SearchQA demonstrate that GraphOPD consistently outperforms eleven baselines, improving the strongest baseline by up to +5.8 percentage points and showing strong causal alignment through an executed‑replay audit.
By Bohan Lin, Liyi Chen, Zhuoning Guo, Muyang Li, Qimeng Wang, Yan Gao, Yao Hu, Yudong Zhang
SOD: Step-wise On-policy Distillation for Small Language Model Agents proposes a new framework that adaptively reweights distillation strength at each reasoning step based on step-level divergence. This approach mitigates cascading errors in tool-integrated reasoning by attenuating misleading teacher signals in high-divergence regions while preserving dense guidance where student and teacher align. Experiments on math, science, and code benchmarks show up to 20.86% improvement over the second-best baseline, with a 0.6B student scoring 26.13% on AIME 2025.
By Qiyong Zhong, Mao Zheng, Mingyang Song, Xin Lin, Jie Sun, Houcheng Jiang, Xiang Wang, Junfeng Fang
Persistent Teacher Anchoring (PTA) is a method that extends on‑policy knowledge distillation by ensuring that a teacher verifies entire turns before any tool calls are executed. PTA builds on chunk‑level verification with an added turn‑level commitment, treating verified chunks as atomic units and introducing persistent lookahead to keep rollout capacity full. Experiments on Search‑R1 and DeepEyes show that PTA improves macro best@4 by 2.5–2.8 points over standard OPKD and boosts throughput by 24%.
By Hyun Bin Park (Sogang University), Kyungho Song (University of Michigan, Ann Arbor), Sangmin Lee (Sogang University), Du-Seong Chang (Sogang University)
arXiv:2610.19951v1 Announce Type: cross
Abstract: Agent skills adapt frozen models through editable external instructions. Revising several rules at once makes changes difficult to inspect, while jud...
By Jia Tong, Senyou Deng, Min Peng, Jianfeng Pan
The paper introduces AMBER, an append‑only memory framework for language‑model agents that interact over long horizons. AMBER lets agents jointly learn to reason, act, and write free‑form memory, guaranteeing retention by construction and enabling end‑to‑end reinforcement learning without extensive curated data. Experiments on WebArena Lite show AMBER outperforms overwrite‑based memory by 4.09 percentage points in average success and improves task completion rates in repeated runs.
By Chinmay Savadikar, Zhaoyu Zhang, Mingyu Zhao, Shuang Xie, Han Li, Tianfu Wu, Lingyun Wang