SynCo is a data synthesis co‑training framework that enables self‑evolving large language models (LLMs) to improve autonomously. It employs two agents—a Synthesizer that generates training tasks based on the Reasoner’s evolving capabilities, and a Reasoner that learns from these tasks—using multi‑agent reinforcement learning to jointly optimize task quality and model performance. Experiments on eight mathematical reasoning benchmarks show that SynCo outperforms existing synthetic‑data methods, especially on previously unsolved problems.
By Wei Yang, Shawn Li, Yuehan Qin, Yawei Wang, Mingxi Wang, Shixuan Li, Tiankai Yang, Jiate Li, Jesse Thomason, Xuezhe Ma, Yue Zhao
The paper introduces TerminalHorizon, an automated engine that synthesizes long‑horizon trajectories for terminal agents by reconstructing environments from real‑world professional work and progressively expanding tasks to require further exploration and improvement. Using this engine, the authors create 1.5K task environments and fine‑tune Qwen3.5‑27B, producing TerminalHorizon‑27B, which shows significant performance gains across 12 benchmarks in coding, agentic production tasks, and reasoning. Case studies demonstrate the model’s ability to experiment beyond initial solutions, refine methods based on feedback, and preserve effective improvements in final deliverables.
By Shun Zou, Ziyu Ma, Yi Zou, Yong Wang, Lin Chen, Zehui Chen, Guanhua Chen, Xiangxiang Chu, Feng Zhao
arXiv:2609.05576v1 Announce Type: new
Abstract: The paradigm of LLMs has rapidly shifted from passive language interfaces to autonomous Claw-like agents that execute long-horizon tasks across statefu...
By Yirong Zeng, Shen You, Jinhang Feng, Yufei Liu, Xiao Ding, Yutai Hou, Hao Cong, Yuxian Wang, Wu Ning, Wang Xu, Bibo Cai
arXiv:2606. 04261v1 Announce Type: new Abstract: Curating training data is among the most consequential yet labor-intensive parts of modern AI development: practitioners iteratively propose, implement, evaluate, and revise data policies against noisy benchmark feedback.
By Feiyang Kang, Hanze Li, Adam Nguyen, Mahavir Dabas, Jiaqi W. Ma, Frederic Sala, Dawn Song, Ruoxi Jia
arXiv:2511. 20297v2 Announce Type: replace Abstract: Large Language Model (LLM)-based agents are increasingly capable of complex, multi-step tasks such as GUI automation, tool use, and data manipulation, yet they cannot learn from experience: each new session rediscovers solutions from scratch.
By Shashank Kirtania, Param Biyani, Priyanshu Gupta, Yasharth Bajpai, Roshni Iyer, Sumit Gulwani, Gustavo Soares
DynaWeave is an automated framework that converts static tasks into dynamic scenarios by generating exogenous events and using DAGs to manage dependencies. It activates these events during agent interaction and employs a dynamic verifier that updates only relevant evaluation checks. Using DynaWeave, the authors collected over 2,000 verified trajectories, fine‑tuned Qwen3.5 models, and achieved significant performance gains on Gaia2, CostBench, and ToolMaze, even surpassing Gemini‑3.1‑Pro‑Preview on ToolMaze.
By Xuan Yang, Chunxiao Liu, Hongsheng Xin, Hao Xu, Ning Miao