arXiv:2604. 17931v3 Announce Type: replace Abstract: Reinforcement Learning (RL) has emerged as a powerful training paradigm for LLM-based agents.
By Wanli Li, Bince Qu, Bo Pan, Jianyu Zhang, Zheng Liu, Pan Zhang, Wei Chen, Bo Zhang
arXiv:2610. 21055v1 Announce Type: new Abstract: Recursive self-improvement, in which AI systems build stronger AI systems, is one promising route to superintelligence.
By Yufa Zhou, Yichen Wang, Jiakun Fan, Ziqi Shu, Zhichao Liu, Anru R. Zhang, Neiwen Ling, Zijun Wei
arXiv:2610.16998v1 Announce Type: new
Abstract: Deep search enables language agents to solve complex information-seeking tasks through iterative web exploration. However, developing robust open-sourc...
By Cong Pang, Jiaqi Su, Chengrui Wu, Kuihao Yan, Tianrui Zhang, Zixuan Qiu, Chao Lin, Zixiu Lin, Zixuan Chen, Zelin Xiao, Tiankuo Yao, Jiawei Hong, Zixuan Chen, Dan Xu, Lewei Lu, Wenjie Wang, Xin Lou
ZGCM-1 is a 7B dense foundation model trained from scratch with extreme data, system, and algorithmic efficiency. It uses a core premise that compact models can overcome capacity limits by combining deliberate internal thinking with active external tool use, supported by a 256K context and an end‑to‑end high‑efficiency training recipe that includes interleaved gated sliding‑window and full attention, a stable FP8 Muon optimizer, progressive curriculum scaling, and reformulation of interaction traces into Markov Decision Processes. The model is competitive with much larger frontier models on challenging mathematical reasoning and agentic search tasks, offers a ~4.2× efficiency improvement in pre‑training time‑to‑loss, and its weights, checkpoints, training code, data recipes, and logs are fully open‑source to support community research.
By Jiyan He, Guang Liang, Hao Liu, Haoxiang Guan, Jinbo Sun, Junyi Guo, Wenjun Feng, Yantai Xie, Yifei Shen, Bin Shao, Chuyang Wei, Kai Chen, Kexin Zhou, Minghang Zhu, Shuxin Zheng, Tie-Yan Liu, Taine Zhao, Wenhui Zhu, Xueyin Xu, Xiaoqing Zhang, Yatao Li, Yuxuan Ren
TMax is an open reinforcement‑learning recipe that trains terminal‑using agents from small language models, achieving 33% on Terminal‑Bench 2.0 with only 9B parameters and outperforming larger prior models. The approach generates training data via a novel taxonomy that blends difficulty control, personas, and verifier diversification, enabling cheap creation of many terminal environments for both RL and supervised fine‑tuning. The recipe is applicable to models ranging from 2B to 27B across different families, dominating the Pareto curve for open terminal‑agent performance at smaller sizes.
By Hamish Ivison, Junjie Oscar Yin, Rulin Shao, Teng Xiao, Nathan Lambert, Hannaneh Hajishirzi
arXiv:2601.13247v2 Announce Type: replace-cross
Abstract: Current Large Language Models (LLMs) exhibit a critical modal disconnect: they possess vast semantic knowledge but lack the procedural ground...
By Baochang Ren, Yunzhi Yao, Rui Sun, Shuofei Qiao, Ningyu Zhang, Huajun Chen