arXiv:2602.19049v2 Announce Type: replace-cross 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 e
The increasing reliance of large language models on complex chains of thought is driving a critical need for more efficient inference, making token optimization a timely research area.
Improving token efficiency significantly reduces the computational costs and environmental footprint of advanced AI models, making them more scalable and accessible for broader applications.
This research introduces a novel, information-theoretic approach to post-training optimization, potentially leading to more intelligent and resource-efficient allocation of reasoning effort within AI models.
- · AI developers
- · Cloud providers
- · SaaS companies utilizing LLMs
- · AI-driven industries
- · Inefficient large language models
- · Users with high inference costs
Large language models become more cost-effective and faster to operate at scale.
Reduced inference costs could enable broader deployment of complex AI agents and services in resource-constrained environments.
Increased efficiency might accelerate the development of more sophisticated and autonomous AI systems, pushing the boundaries of AI capabilities.
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Read at arXiv cs.LG