RLoop: An Self-Improving Framework for Reinforcement Learning with
  Iterative Policy Initialization
Authors: name: Zeng Zhiyuan, name: Jiashuo Liu, name: Zhangyue Yin, name: Ge Zhang, name: Wenhao Huang, name: Xipeng Qiu

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.