IIB-LPO: Latent Policy Optimization via Iterative Information Bottleneck

Understand

Recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) for Large Language Model (LLM) reasoning have been hindered by a persistent challenge: exploration collapse.

• The semantic homogeneity of random rollouts often traps models in narrow, over-optimized behaviors.
• While existing methods leverage policy entropy to encourage exploration, they face inherent limitations.
• Global entropy regularization is susceptible to reward hacking, which can induce meaningless verbosity, whereas local token-selective updates struggle with the strong inductive bias of pre-trained models.