arXiv:2606. 08340v1 Announce Type: new Abstract: As language models are increasingly deployed as autonomous agents, they must coordinate with others over long horizons in open-ended interactive tasks.
By Kale-ab Abebe Tessera, Andras Szecsenyi, Cameron Barker, Alexander Rutherford, Davide Paglieri, Aidan Scannell, Henry Gouk, Elliot J. Crowley, Tim Rockt\"aschel, Amos Storkey
G2MAF is a test‑time refinement framework for offline multi‑agent reinforcement learning that applies a single globally normalized, projected critic gradient to adjust all agents’ actions while keeping them close to a frozen policy proposal. The method improves performance on 24 Multi‑Party Environment (MPE) and StarCraft Multi‑Agent Challenge (SMAC) benchmarks, achieving mean relative gains of 9.2% on MPE and 8.9% on SMAC, with only a 6% increase in inference latency.
By Guowei Zou, Haitao Wang, Guoxin Wang, Zhiquan Chen, Beiwen Zhang, Guojie Wang, Hejun Wu
arXiv:2512. 11213v2 Announce Type: replace Abstract: Scaling test-time computation has been shown to significantly improve large language model (LLM) performance without additional training.
By Dongwon Jung, Peng Shi, Muhao Chen, Yi Zhang
arXiv:2605.01457v4 Announce Type: replace
Abstract: How can generative offline multi-agent reinforcement learning achieve both fast joint trajectory generation and effective cooperation? Multi-agent...
By Guowei Zou, Haitao Wang, Beiwen Zhang, Boning Zhang, Hejun Wu
arXiv:2606. 31073v1 Announce Type: new Abstract: Large language models (LLMs) provide a promising interface for high-level robotic task planning, but their use in multi-UAV collaboration remains difficult to evaluate systematically.
By Sheng Zhang, Qinglin Li, Yuechao Zang, Xueqin Huang, Yijia Fu, Cheng Zhu
arXiv:2510. 05592v2 Announce Type: replace Abstract: Outcome-driven reinforcement learning has advanced reasoning in large language models (LLMs), but prevailing tool-augmented approaches train a single, monolithic policy that interleaves thoughts and tool calls under full context; this scales poorly with long horizons and diverse tools and generalizes weakly to new scenarios.
By Zhuofeng Li, Haoxiang Zhang, Seungju Han, Sheng Liu, Jianwen Xie, Yu Zhang, Yejin Choi, James Zou, Pan Lu