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

IAPO: Information-Aware Policy Optimization for Token-Efficient Reasoning arXiv:2602.19049v2 Announce Type: replace 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 informati