Despite recent advances in Reinforcement learning with verifiable rewards (RLVR) for large language model (LLM) reasoning, most methods suffer from exploration collapse, as the semantic homogeneity of random rollouts traps models in narrow, over-optimized behaviors. Existing methods leverage policy entropy to encourage exploration, but face inherent limitations: global entropy regularization is susceptible to reward hacking, inducing meaningless verbosity, whereas local token-selective updates struggle with the strong inductive bias of pre-trained models. To this end, we propose Latent Policy Optimization via Iterative Information Bottleneck ( I²B-LPO), which shifts from statistical perturbation of token distributions to topological branching of reasoning trajectories. I²BLPO triggers latent branching at high-entropy states to diversify reasoning trajectories and applies the Information Bottleneck as a trajectory filter and self-reward to ensure concise and informative exploration. Empirical results on four mathematical benchmarks demonstrate that I²B-LPO achieves state-of-the-art performance, with margins of up to 5.3% in accuracy and 7.4% in diversity metrics. Code is available at https://github.com/denghuilin-cyber/IIB-LPO.

Optimizing data utilization remains a central challenge in applying Reinforcement Learning (RL) to Large Language Models (LLMs), directly impacting sample efficiency, training stability, and final model performance.Current approaches often rely on massive static datasets, leading to computational inefficiency and redundant gradient updates.In this paper, we propose ScalingRL, a data-centric RL framework that dynamically selects the most informative training samples to optimize RL for mathematical reasoning.Specifically, ScalingRL introduces the Data Effectiveness Score (DES) that quantitatively ranks prompts according to three complementary factors: problem difficulty, Chain-of-Thought complexity, and reward adaptability.Then, ScalingRL employs an adaptive curriculum scheduler that progressively adjusts the overall scale and specific mix of training prompts—balancing exploration of new, challenging data with exploitation of previously learned concepts—thereby tailoring the data distribution to the model’s current learning trajectory and performance.Experimental results demonstrate that ScalingRL achieves comparable performance to full-data training methods while requiring only 1.5K samples instead of 220K, reducing training time from 13 days to just 4 hours on 8×A800 GPUs.