Harness Scaling introduces a new scaling dimension for agentic reinforcement learning by training models across diverse harness configurations instead of a single fixed harness. The approach constructs a factorized harness space, selects a compact training subset with balanced factorial coverage, and organizes cross-harness training to combine global coverage with controlled variation for the same task. Experiments across multiple benchmarks, model scales, and RL algorithms show that Harness Scaling consistently improves generalization to unseen white-box configurations and enables effective transfer to unseen external black-box harnesses.
By Fei Bai, Huatong Song, Chuan Hao, Ran Tao, Jian Yang, Bryan Dai, Wayne Xin Zhao, Ji-Rong Wen
The paper introduces a new multi‑agent reinforcement learning framework that separates agent identity from behavior by using events to trigger behavioral changes. It proposes Neural Manifold Diversity (NMD) to create a continuous behavior manifold and an event‑based hypernetwork that generates Low‑Rank Adaptation (LoRA) modules for on‑the‑fly policy reconfiguration. The authors prove that this design preserves reward maximization and show empirically that it outperforms baselines, generalizes zero‑shot, and uniquely solves tasks requiring sequential behavior reassignment.
By Hannes B\"uchi, Manon Flageat, Eduardo Sebasti\'an, Amanda Prorok
arXiv:2608. 19684v1 Announce Type: new Abstract: Recent studies investigate how to leverage pre-collected datasets to improve the policy performance and sample efficiency of RL.
By Tanachai Anakewat, Takayuki Osa, Tatsuya Harada
The paper introduces the Agentic Compositional Generalization hypothesis, suggesting that reinforcement learning (RL) primarily refines high‑level decision‑making behaviors that orchestrate pre‑trained low‑level skills, rather than teaching new domain‑specific skills from scratch. It proposes River, a training recipe that enhances reward quality by filtering low‑quality synthetic environments and adding process‑level behavior regularization. Using River, RL‑trained agents outperform other open‑source 8B models on four terminal‑agent benchmarks, achieving significant gains with fewer than 30% of the training environments.
By Yihang Yao, Bo Pang, Xuan Phi Nguyen, Ding Zhao, Shafiq Joty, Semih Yavuz
arXiv:2512. 09706v2 Announce Type: replace Abstract: The paradigm of agentic AI is shifting from engineered complex workflows to post-training native models.
By Kaichen He, Zihao Wang, Muyao Li, Anji Liu, Yitao Liang
arXiv:2502. 19544v3 Announce Type: replace Abstract: Leveraging offline data is a promising way to improve the sample efficiency of online reinforcement learning (RL).
By Yi Zhao, Aidan Scannell, Wenshuai Zhao, Yuxin Hou, Tianyu Cui, Le Chen, Dieter B\"uchler, Arno Solin, Juho Kannala, Joni Pajarinen