IAPO: Information-Aware Policy Optimization for Token-Efficient Reasoning

• URL: http://arxiv.org/abs/2602.19049v1
• Date: Sun, 22 Feb 2026 05:30:14 GMT
• Title: IAPO: Information-Aware Policy Optimization for Token-Efficient Reasoning
• Authors: Yinhan He, Yaochen Zhu, Mingjia Shi, Wendy Zheng, Lin Su, Xiaoqing Wang, Qi Guo, Jundong Li,
• Abstract summary: We argue that existing sequence-level reward-shaping methods offer limited control over how reasoning effort is allocated across tokens.<n>We propose IAPO, an information-theoretic post-training framework that assigns token-wise advantages based on each token's conditional mutual information.<n>IAPO consistently improves reasoning accuracy while reducing reasoning length by up to 36%, outperforming existing token-efficient RL methods.
• Score: 47.55414301744048
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models increasingly rely on long chains of thought to improve accuracy, yet such gains come with substantial inference-time costs. We revisit token-efficient post-training and argue that existing sequence-level reward-shaping methods offer limited control over how reasoning effort is allocated across tokens. To bridge the gap, we propose IAPO, an information-theoretic post-training framework that assigns token-wise advantages based on each token's conditional mutual information (MI) with the final answer. This yields an explicit, principled mechanism for identifying informative reasoning steps and suppressing low-utility exploration. We provide a theoretical analysis showing that our IAPO can induce monotonic reductions in reasoning verbosity without harming correctness. Empirically, IAPO consistently improves reasoning accuracy while reducing reasoning length by up to 36%, outperforming existing token-efficient RL methods across various reasoning datasets. Extensive empirical evaluations demonstrate that information-aware advantage shaping is a powerful and general direction for token-efficient post-training. The code is available at https://github.com/YinhanHe123/IAPO.

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