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arXiv · 2511.11828

Conformal Constrained Policy Optimization for Cost-Effective LLM Agents

Wenwen Si·Sooyong Jang·Insup Lee·Osbert Bastani
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Abstract

While large language models (LLMs) have recently made tremendous progress towards solving challenging AI problems, they have done so at increasingly steep computational and API costs. We propose a novel strategy where we combine multiple LLM models with varying cost/accuracy tradeoffs in an agentic manner, where models and tools are run in sequence as determined by an orchestration model to minimize cost subject to a user-specified level of reliability; this constraint is formalized using conformal prediction to provide guarantees. To solve this problem, we propose Conformal Constrained Policy Optimization (CCPO), a training paradigm that integrates constrained policy optimization with off-policy reinforcement learning and recent advances in online conformal prediction. CCPO jointly optimizes a cost-aware policy (score function) and an adaptive threshold. Across two multi-hop question answering benchmarks, CCPO achieves up to a 30% cost reduction compared to other cost-aware baselines and LLM-guided methods without compromising reliability. Our approach provides a principled and practical framework for deploying LLM agents that are significantly more cost-effective while maintaining reliability.

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Wenwen Si, Sooyong Jang, Insup Lee, Osbert Bastani. 2026-03-23. Conformal Constrained Policy Optimization for Cost-Effective LLM Agents. https://arxiv.org/abs/2511.11828

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