RLoop: An Self-Improving Framework for Reinforcement Learning with Iterative Policy Initialization

Zeng Zhiyuan, Jiashuo Liu, Zhangyue Yin, Ge Zhang, Wenhao Huang, Xipeng Qiu

RLoop: An Self-Improving Framework for Reinforcement Learning with Iterative Policy Initialization: 8 upvotes on Hugging Face Daily Papers, #18 of 31 papers on 2025-11-11. Day-by-day upvote history.

While Reinforcement Learning for Verifiable Rewards (RLVR) is powerful for training large reasoning models, its training dynamics harbor a critical challenge: RL overfitting, where models gain training rewards but lose generalization. Our analysis reveals this is driven by policy over-specialization and catastrophic forgetting of diverse solutions generated during training. Standard optimization discards this valuable inter-step policy diversity. To address this, we introduce RLoop, a self-improving framework built on iterative policy initialization. RLoop transforms the standard training process into a virtuous cycle: it first uses RL to explore the solution space from a given policy, then filters the successful trajectories to create an expert dataset. This dataset is used via Rejection-sampling Fine-Tuning (RFT) to refine the initial policy, creating a superior starting point for the next iteration. This loop of exploration and exploitation via iterative re-initialization effectively converts transient policy variations into robust performance gains. Our experiments show RLoop mitigates forgetting and substantially improves generalization, boosting average accuracy by 9% and pass@32 by over 15% compared to vanilla RL.

Paper page on Hugging Face · arXiv

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