@inproceedings{deng-etal-2026-i2b,
title = "{I}{\texttwosuperior}{B}-{LPO}: Latent Policy Optimization via Iterative Information Bottleneck",
author = "Deng, Huilin and
Luo, Hongchen and
Zhu, Yue and
Li, Long and
Chen, Zhuoyue and
Zhao, Xinghao and
LI, Ming and
Zhao, Chuyang and
Zhang, Jihai and
Wang, MengChang and
Cao, Yang and
Kang, Yu",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1084/",
pages = "23647--23664",
ISBN = "979-8-89176-390-6",
abstract = "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 ( I{\texttwosuperior}B-LPO), which shifts from statistical perturbation of token distributions to topological branching of reasoning trajectories. I{\texttwosuperior}BLPO 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 I{\texttwosuperior}B-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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T I²B-LPO: Latent Policy Optimization via Iterative Information Bottleneck
%A Deng, Huilin
%A Luo, Hongchen
%A Zhu, Yue
%A Li, Long
%A Chen, Zhuoyue
%A Zhao, Xinghao
%A LI, Ming
%A Zhao, Chuyang
%A Zhang, Jihai
%A Wang, MengChang
%A Cao, Yang
%A Kang, Yu
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F deng-etal-2026-i2b
%X 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.
%U https://aclanthology.org/2026.acl-long.1084/
%P 23647-23664
Markdown (Informal)
[I²B-LPO: Latent Policy Optimization via Iterative Information Bottleneck](https://aclanthology.org/2026.acl-long.1084/) (Deng et al., ACL 2026)
ACL
- Huilin Deng, Hongchen Luo, Yue Zhu, Long Li, Zhuoyue Chen, Xinghao Zhao, Ming LI, Chuyang Zhao, Jihai Zhang, MengChang Wang, Yang Cao, and Yu Kang. 2026. I²B-LPO: Latent Policy Optimization via Iterative Information Bottleneck. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 23647–23664, San Diego, California, United States. Association for Computational Linguistics.