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| Autori principali: | , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2604.26733 |
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| _version_ | 1866918502704611328 |
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| author | Han, Zhixin Zhang, Yanzhi Wei, Chuyang Gao, Maohang Yue, Xiawei Chen, Kefei Zhuang, Yu Guan, Haoxiang He, Jiyan Li, Jian Duan, Yitong Shi, Yu Hu, Mengting Zheng, Shuxin |
| author_facet | Han, Zhixin Zhang, Yanzhi Wei, Chuyang Gao, Maohang Yue, Xiawei Chen, Kefei Zhuang, Yu Guan, Haoxiang He, Jiyan Li, Jian Duan, Yitong Shi, Yu Hu, Mengting Zheng, Shuxin |
| contents | Live future prediction refers to the task of making predictions about real-world events before they unfold. This task is increasingly studied using large language model-based agent systems, and it is important for building agents that can continually learn from the real world. It can provide a large number of prediction questions grounded in diverse real-world events, while preventing answer leakage. To leverage the advantages of future prediction, we present FutureWorld, a live agentic reinforcement learning environment that closes the training loop between prediction, outcome realization, and parameter updates. Specifically, we modify and extend verl-tool, resulting in a new framework that we call verl-tool-future. Unlike standard reinforcement learning training frameworks that rely on immediate rewards, verl-tool-future stores prediction-time rollouts, backfills rewards after real-world outcomes become available, and then replays the completed trajectories for policy update. Across three open-source agents, successive FutureWorld training rounds lead to consistent improvements in prediction accuracy, probabilistic scoring, and calibration, demonstrating that delayed real-world outcome feedback can serve as an effective reinforcement learning signal. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_26733 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FutureWorld: A Live Reinforcement Learning Environment for Predictive Agents with Real-World Outcome Rewards Han, Zhixin Zhang, Yanzhi Wei, Chuyang Gao, Maohang Yue, Xiawei Chen, Kefei Zhuang, Yu Guan, Haoxiang He, Jiyan Li, Jian Duan, Yitong Shi, Yu Hu, Mengting Zheng, Shuxin Artificial Intelligence Machine Learning Live future prediction refers to the task of making predictions about real-world events before they unfold. This task is increasingly studied using large language model-based agent systems, and it is important for building agents that can continually learn from the real world. It can provide a large number of prediction questions grounded in diverse real-world events, while preventing answer leakage. To leverage the advantages of future prediction, we present FutureWorld, a live agentic reinforcement learning environment that closes the training loop between prediction, outcome realization, and parameter updates. Specifically, we modify and extend verl-tool, resulting in a new framework that we call verl-tool-future. Unlike standard reinforcement learning training frameworks that rely on immediate rewards, verl-tool-future stores prediction-time rollouts, backfills rewards after real-world outcomes become available, and then replays the completed trajectories for policy update. Across three open-source agents, successive FutureWorld training rounds lead to consistent improvements in prediction accuracy, probabilistic scoring, and calibration, demonstrating that delayed real-world outcome feedback can serve as an effective reinforcement learning signal. |
| title | FutureWorld: A Live Reinforcement Learning Environment for Predictive Agents with Real-World Outcome Rewards |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2604.26733 |