ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning
arXiv:2602.21534v1 Announce Type: new Abstract: Agentic reinforcement learning (ARL) has rapidly gained attention as a promising paradigm for training agents to solve complex, multi-step interactive tasks. Despite encouraging early results, ARL remains highly unstable, often leading to training collapse. This instability limits scalability to larger environments and longer interaction horizons, and constrains systematic exploration of algorithmic design choices. In this paper, we first propose ARLArena, a stable training recipe and systematic analysis framework that examines training stability in a controlled and reproducible setting. ARLArena first constructs a clean and standardized testbed. Then, we decompose policy gradient into four core design dimensions and assess the performance and stability of each dimension. Through this fine-grained analysis, we distill a unified perspective on ARL and propose SAMPO, a stable agentic policy optimization method designed to mitigate the domi
arXiv:2602.21534v1 Announce Type: new Abstract: Agentic reinforcement learning (ARL) has rapidly gained attention as a promising paradigm for training agents to solve complex, multi-step interactive tasks. Despite encouraging early results, ARL remains highly unstable, often leading to training collapse. This instability limits scalability to larger environments and longer interaction horizons, and constrains systematic exploration of algorithmic design choices. In this paper, we first propose ARLArena, a stable training recipe and systematic analysis framework that examines training stability in a controlled and reproducible setting. ARLArena first constructs a clean and standardized testbed. Then, we decompose policy gradient into four core design dimensions and assess the performance and stability of each dimension. Through this fine-grained analysis, we distill a unified perspective on ARL and propose SAMPO, a stable agentic policy optimization method designed to mitigate the dominant sources of instability in ARL. Empirically, SAMPO achieves consistently stable training and strong performance across diverse agentic tasks. Overall, this study provides a unifying policy gradient perspective for ARL and offers practical guidance for building stable and reproducible LLM-based agent training pipelines.
Executive Summary
This article proposes ARLArena, a unified framework for stable agentic reinforcement learning (ARL). ARLArena addresses the instability issue in ARL, a critical problem that limits scalability and systematic exploration of algorithmic design choices. The authors decompose policy gradient into four core design dimensions and propose SAMPO, a stable agentic policy optimization method. Through empirical evaluation, SAMPO demonstrates consistently stable training and strong performance across diverse agentic tasks. ARLArena offers practical guidance for building stable and reproducible large language model (LLM)-based agent training pipelines, providing a unifying policy gradient perspective for ARL. This study has significant implications for the development of more robust and efficient ARL methods, which can be applied to a wide range of applications, including robotics, finance, and healthcare.
Key Points
- ▸ ARLEnna: A unified framework for stable agentic reinforcement learning
- ▸ Policy gradient decomposition into four core design dimensions
- ▸ SAMPO: A stable agentic policy optimization method
- ▸ Empirical evaluation demonstrates stability and strong performance across diverse tasks
Merits
Strength in Methodology
The authors employ a systematic and controlled approach to examine training stability, which is crucial for reproducibility and scalability.
Unified Perspective on ARL
The study provides a unifying policy gradient perspective for ARL, which can facilitate the development of more robust and efficient ARL methods.
Demerits
Limited Generalizability
The study focuses on a specific type of ARL, and it is unclear whether the results can be generalized to other types of ARL or more complex tasks.
Need for Further Evaluation
While SAMPO demonstrates promising results, further evaluation and testing are necessary to confirm its effectiveness and robustness in various scenarios.
Expert Commentary
The article presents a comprehensive and systematic approach to addressing the instability issue in ARL. The authors' use of ARLArena, a unified framework for stable ARL, is a significant contribution to the field. The decomposition of policy gradient into four core design dimensions and the proposal of SAMPO, a stable agentic policy optimization method, demonstrate a deep understanding of the underlying challenges. However, the study's limitations, such as the need for further evaluation and testing, should be addressed in future research. Nonetheless, this study has the potential to revolutionize the development of more robust and efficient ARL methods, which can be applied to a wide range of applications.
Recommendations
- ✓ Future studies should focus on evaluating the generalizability of the results and the robustness of SAMPO across different scenarios.
- ✓ Researchers should investigate the application of ARLArena and SAMPO in more complex tasks and domains, such as robotics and finance.
