The paper introduces Trajectory-guided Joint Policy Optimization (TJPO), a reinforcement‑learning framework that explicitly encourages diversity in the trajectories of large‑language‑model agents. By defining task‑specific trajectory descriptors, TJPO measures and optimizes diversity as a set‑level function, avoiding population‑based training. Experiments on Sokoban and ALFWorld demonstrate that TJPO yields diverse, interpretable behaviors while preserving strong task performance.
By Huaiyu Fu, Heng Cao, Hao Wang, Jian Ya, Tao Chen
The paper investigates zero‑shot task generalisation in offline multi‑agent reinforcement learning by extending sequence‑modeling architectures to support multi‑task observation and action spaces and variable agent counts. It finds that increasing task diversity, rather than merely enlarging the dataset, is the key driver for robust zero‑shot transfer. Experiments on four challenging environments show a 3.2× mean improvement on held‑out tasks compared to single‑task models and outperform strong behaviour‑cloning baselines.
By Oussama Hidaoui, Omer Ebead, Ulrich Armel Mbou Sob, Siddarth Singh, Juan Claude Formanek, Felix Chalumeau, Omayma Mahjoub, Sasha Abramowitz, Ruan John de Kock, Wiem Khlifi, Louay Ben Nessir, Simon Verster Du Toit, Daniel Rajaonarivonivelomanantsoa, Asim Awad Osman, Arnol Manuel Fokam, Refiloe Shabe, Arnu Pretorius
arXiv:2609.06586v1 Announce Type: cross
Abstract: A shared reward gives agents a common objective, but leaves open when, how and even whether they must cooperate to succeed. We address these question...
By Yannick Molinghen, Hugo Charels, Tom Lenaerts
arXiv:2603. 27044v3 Announce Type: replace-cross Abstract: Deep Reinforcement Learning (DRL) is widely recognized as sample-inefficient, a limitation attributable in part to the high dimensionality and substantial functional redundancy inherent to the policy parameter space.
By Andrea Fraschini, Davide Tenedini, Riccardo Zamboni, Mirco Mutti, Marcello Restelli
arXiv:2610.18808v1 Announce Type: cross
Abstract: In cooperative Multi-Agent Reinforcement Learning (MARL), agents must learn to coordinate their actions to achieve shared objectives. A key indicator...
By Svar Rajankumar Patel, Kshitij Jerath
arXiv:2610.15969v1 Announce Type: new
Abstract: In multi-agent reinforcement learning, exposing a cooperator to partners with diverse strategies during training is key to better collaboration at test...
By Paul Teiletche, Kanghyun Ryu, Kyungjoon Park, Dhruva Tirumala, Markus Wulfmeier, Negar Mehr
arXiv:2607. 17760v1 Announce Type: cross Abstract: Inverse reinforcement learning (IRL) provides a powerful framework for learning from demonstrations.
By Ziyi Liu, Grace Zhang
arXiv:2606. 24601v1 Announce Type: new Abstract: Multi-agent reinforcement learning (MARL) addresses the problem of training multiple agents that pursue collaborative, competitive, or mixed objectives.
By Anurag Akula, Satheesh K. Perepu, Abhishek Sarkar, Kaushik Dey
The paper introduces Meta-Gradient Informed Multi-Objective Multi-Agent Reinforcement Learning (MIRL), a framework that leverages the structure of multiple objectives to enhance cooperation among agents. By formulating MOMARL at both team and individual levels and using objective-wise counterfactual credit assignment, MIRL allows agents to learn how to prioritize objectives individually while jointly optimizing a system-level objective. Experiments on a multi-objective particle environment demonstrate that MIRL, especially its dynamic variant, improves Pareto-front quality compared to baseline methods.
By Qiyue Xia, J. Michael Herrmann
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
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
The paper introduces RePPO, a method that adapts Proximal Policy Optimization to jointly optimize a compact population of diverse, high‑quality policies using a population‑dependent, dynamically shaped reward. RePPO achieves a strong quality‑diversity tradeoff on standard continuous‑control tasks and scales to larger policy and behavior spaces, maintaining quality while significantly increasing diversity in pixel‑based tasks. The authors also demonstrate RePPO’s applicability to fine‑tuning large language models, showing for the first time that quality‑diversity reinforcement learning can scale to this domain.
By Saeed Hedayatian, Harsh Shah, Celine Mneimneh, Stefanos Nikolaidis