arXiv:2507.18059v2 Announce Type: replace Abstract: Due to practical constraints such as partial observability and limited communication, Centralized Training with Decentralized Execution (CTDE) has become the dominant paradigm in cooperative Multi-Agent Reinforcement Learning (MARL). However, existing CTDE methods often underutilize centralized training or lack theoretical guarantees. We propose Multi-Agent Guided Policy Optimization (MAGPO), a novel framework that better leverages centralized training by integrating centralized guidance with decentralized execution. MAGPO uses an autoregressive joint policy for scalable, coordinated exploration and explicitly aligns it with decentralized policies to ensure deployability under partial observability. We provide theoretical guarantees of monotonic policy improvement and empirically evaluate MAGPO on 43 tasks across 6 diverse environments. Results show that MAGPO consistently outperforms strong CTDE baselines and matches or surpasses fully centralized approaches, offering a principled and practical solution for decentralized multi-agent learning. Our code and experimental data can be found in https://github.com/liyheng/MAGPO.

arXiv:2505.15418v2 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) in partially observable environments poses significant challenges due to the complexity of learning under uncertainty. While additional information, such as that available in simulations, can enhance training, effectively leveraging it remains an open problem. To address this, we introduce Guided Policy Optimization (GPO), a framework that co-trains a guider and a learner. The guider takes advantage of privileged information while ensuring alignment with the learner's policy that is primarily trained via imitation learning. We theoretically demonstrate that this learning scheme achieves optimality comparable to direct RL, thereby overcoming key limitations inherent in existing approaches. Empirical evaluations show strong performance of GPO across various tasks, including continuous control with partial observability and noise, and memory-based challenges, significantly outperforming existing methods.

arXiv:2603.00171v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) often struggle to accurately perceive fine-grained visual details, especially when targets are tiny or visually subtle. This challenge can be addressed through semantic-visual information fusion, which integrates global image context with fine-grained local evidence for multi-scale visual understanding. Recently, a paradigm termed "Thinking with Images" has emerged, enabling models to acquire high-resolution visual evidence by zooming or cropping image regions and fusing these local details with global context during reasoning. Although training-based approaches demonstrate the effectiveness of this capability, they require extensive computational resources and large-scale task-specific data. Consequently, lightweight training-free methods have been proposed as a practical alternative to incorporate local visual evidence during inference. However, existing training-free approaches still suffer from two key limitations. First, they indiscriminately extract and fuse local visual regions for all inputs regardless of necessity, introducing computational redundancy and perceptual noise. Second, they exhibit drift between semantic intent and visual attention, preventing accurate localization of user-focused regions. To address these challenges, we propose SvfEye, a training-free framework for adaptive visual-semantic fusion. SvfEye follows a two-stage pipeline with a confidence-based decision module to determine whether additional local visual information is needed, and a semantic-attention fusion module to identify informative local regions. Experiments show that SvfEye achieves substantial performance gains while obtaining an approximately 4.0x inference speedup over the state-of-the-art method ZoomEye.