FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning
The article introduces FBOS-RL, a new framework for reinforcement learning that enhances training efficiency. It combines Feedback-Guided Exploration Enhancement with two training objectives: Exploitation-oriented Policy Alignment and Exploration-oriented Capability Cultivation. Experimental results show that FBOS-RL significantly outperforms existing methods in both speed and final performance.
- ▪FBOS-RL addresses limitations in traditional reinforcement learning methods like GRPO.
- ▪The framework utilizes feedback from the environment to improve exploration and policy alignment.
- ▪Extensive experiments indicate that FBOS-RL achieves faster learning and higher performance ceilings.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20256 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Machine Learning arXiv:2605.20256 (cs) [Submitted on 18 May 2026] Title:FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning Authors:Xikai Zhang, Yongzhi Li, Likang Xiao, Yingze Zhang, Yanhua Cheng, Quan Chen, Peng Jiang, Wenjun Wu, Liu Liu View a PDF of the paper titled FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning, by Xikai Zhang and 8 other authors View PDF HTML (experimental) Abstract:Reinforcement learning has become a cornerstone for aligning and unlocking the reasoning capabilities of large-scale models. At its core, the training loop of GRPO and its variants alternates between rollout sampling and policy update.
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