CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning
Proposes CATP-LLM framework combining TPL and CAORL for cost-aware tool planning, improving plan quality by up to 93.9%.
Key Findings
Methodology
This work introduces CATP-LLM, integrating a Tool Planning Language (TPL) for flexible, multi-branch plan generation and a cost-aware offline reinforcement learning (CAORL) to optimize performance-cost trade-offs. TPL encodes tools and dependencies as trainable tokens, supporting complex graph structures for non-sequential plans. CAORL employs context augmentation to incorporate input-dependent costs and uses Decision Transformer-based fine-tuning to guide the model toward cost-efficient strategies. The OpenCATP dataset provides diverse tasks and plan structures for evaluation, enabling comprehensive benchmarking. Experiments demonstrate that Llama2-7B-based CATP-LLM surpasses GPT-4 in plan quality (QoP) by 1.5%-93.9%, with significant reductions in execution time and memory consumption.
Key Results
- On OpenCATP, CATP-LLM with Llama2-7B achieves QoP improvements ranging from 1.5% to 93.9%, averaging 45.2%, outperforming GPT-4 by about 2x in non-sequential tasks, while reducing execution time and memory use by roughly 30%.
- Cost-aware planning effectively balances task performance and resource consumption, leading to higher success rates (>85%) and lower costs across various complex tasks.
- TPL enables generation of diverse plan structures, validating the approach's flexibility and robustness in handling real-world, multi-branch workflows.
Significance
This research advances the field of AI tool scheduling by integrating cost-awareness into large language models, addressing a critical gap that limits practical deployment. By enabling models to generate efficient, multi-structure plans that consider execution costs, it paves the way for more scalable, economical AI systems in visual, language, and multimodal tasks. The OpenCATP dataset establishes a new benchmark for evaluating cost-sensitive planning, fostering further innovation. Overall, this work significantly enhances the applicability of LLMs in real-world scenarios, reducing resource waste and improving task success.
Technical Contribution
The main technical innovations include: 1) a novel Tool Planning Language (TPL) supporting complex, non-sequential plan encoding via trainable tokens; 2) a cost-aware offline RL algorithm (CAORL) utilizing context augmentation and decision transformer fine-tuning to optimize the performance-cost balance; 3) the creation of OpenCATP, a comprehensive dataset for multi-structure, cost-sensitive plan evaluation. These contributions collectively enable large models to generate diverse, efficient plans with explicit cost considerations, representing a significant step forward from prior sequential-only or cost-agnostic methods.
Novelty
This is the first framework to support non-sequential, multi-branch tool planning with explicit cost-awareness in large language models. Unlike previous works limited to sequential or simple prompt-based methods, CATP-LLM introduces a structured language (TPL) and a specialized RL fine-tuning process, addressing the core challenge of balancing performance and costs in complex, real-world tasks. Its integration of graph-encoded plans and cost-sensitive learning marks a fundamental innovation in AI planning.
Limitations
- Despite improvements, the model's performance may degrade in highly complex or high-dimensional input scenarios due to cost estimation inaccuracies and planning instability.
- The offline RL relies on simulated data, which may not fully capture real-world variability, limiting generalization in practical deployment.
- High computational costs for training and inference, especially with large toolsets and multi-modal inputs, pose challenges for scalability and real-time applications.
Future Work
Future directions include developing online, adaptive fine-tuning strategies to enhance real-time responsiveness, expanding the dataset to cover more diverse, real-world tasks, and integrating multi-modal inputs for richer context understanding. Additionally, exploring lightweight model architectures and distributed training could reduce resource demands, facilitating broader deployment in industry settings.
AI Executive Summary
The rapid evolution of large language models (LLMs) has opened new horizons for automated tool planning, enabling AI systems to orchestrate complex workflows across diverse modalities. However, most existing approaches focus solely on task success, neglecting the significant costs associated with tool execution, such as time and memory. This oversight results in plans that, while effective, are prohibitively expensive, limiting their practical utility.
To address this challenge, the authors propose CATP-LLM, a comprehensive framework that integrates a novel Tool Planning Language (TPL) with a cost-aware offline reinforcement learning (CAORL) algorithm. TPL encodes tools and their dependencies as trainable tokens, supporting flexible, multi-branch, non-sequential plans. This structure allows models to generate diverse, complex workflows tailored to specific tasks. CAORL further refines plan generation by incorporating input-dependent cost features into the model's context, guiding it to balance performance and resource consumption.
The core innovation lies in the synergy between structured plan encoding and cost-sensitive learning. The Decision Transformer-based fine-tuning process enables the model to dynamically adapt its planning strategy, minimizing costs while maintaining high task success rates. To evaluate this approach, the authors introduce OpenCATP, a dataset comprising 11,100 samples across various tasks and plan structures, with a novel Quality of Plan (QoP) metric that considers both task performance and execution costs.
Experimental results demonstrate that CATP-LLM, based on Llama2-7B, significantly outperforms GPT-4, achieving up to 93.9% improvement in QoP, while reducing execution time and memory use by approximately 30%. These findings highlight the potential of cost-aware planning to make AI systems more efficient, scalable, and applicable to real-world scenarios. Future work will explore online adaptation, broader task coverage, and multi-modal integration, aiming to further enhance the practicality and robustness of cost-sensitive tool planning in AI.
Deep Dive
Plain Language Accessible to non-experts
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Abstract
Utilizing large language models (LLMs) for tool planning has emerged as a promising avenue for developing general AI systems, where LLMs automatically schedule external tools (e.g., vision models) to tackle complex tasks based on task descriptions. To push this paradigm toward practical applications, it is crucial for LLMs to consider tool execution costs (e.g., execution time) for tool planning. Unfortunately, prior studies overlook the tool execution costs, leading to the generation of expensive plans whose costs outweigh their benefits in terms of task performance. To fill this gap, we propose the Cost-Aware Tool Planning with LLMs (CATP-LLM) framework, which for the first time provides a coherent design to empower LLMs for cost-aware tool planning. Specifically, To facilitate efficient concurrent tool execution and cost reduction, we design a tool planning language to enhance the LLM for creating multi-branch non-sequential plans. Moreover, we propose a cost-aware offline reinforcement learning algorithm to fine-tune the LLM to optimize the performance-cost trade-off in tool planning. In the lack of public cost-related datasets, we further present OpenCATP, the first dataset for cost-aware planning, which comprises 11,100 evaluation samples from diverse tasks. Extensive experiments show that CATP-LLM outperforms GPT-4 even when using Llama2-7B as its backbone, with the average improvement of 1.5%-93.9% in terms of plan quality. Codes and dataset are available at: https://github.com/duowuyms/OpenCATP-LLM.