Token-Budget-Aware LLM Reasoning (2412.18547v5)

Abstract: Reasoning is critical for LLMs to excel in a wide range of tasks. While methods like Chain-of-Thought (CoT) reasoning and enhance LLM performance by decomposing problems into intermediate steps, they also incur significant overhead in token usage, leading to increased costs. We find that the reasoning process of current LLMs is unnecessarily lengthy and it can be compressed by including a reasonable token budget in the prompt, but the choice of token budget plays a crucial role in the actual compression effectiveness. We then propose a token-budget-aware LLM reasoning framework that dynamically adjusts the number of reasoning tokens based on the reasoning complexity of each problem. Experiments show that our method effectively reduces token costs in CoT reasoning with only a slight performance reduction, offering a practical solution to balance efficiency and accuracy in LLM reasoning. Code: https://github.com/GeniusHTX/TALE

• The paper introduces a token-budget framework that dynamically estimates token usage based on problem complexity to reduce redundancy in Chain-of-Thought reasoning.
• The methodology constrains the reasoning process with a preset token budget, maintaining high accuracy with less than a 5% performance drop while cutting token usage by up to 68.64%.
• The findings highlight a significant cost-efficiency improvement and set the stage for integrating advanced token management in future large language model architectures.

Token-Budget-Aware LLM Reasoning: A Framework for Optimizing Efficiency in LLMs

"Token-Budget-Aware LLM Reasoning" presents a novel approach aimed at optimizing the reasoning efficiency of LLMs. The paper addresses a pertinent challenge in LLM deployment: the increased token usage resultant from reasoning techniques like Chain-of-Thought (CoT) which, while enhancing performance, also incur substantial computational and financial costs. This research proposes a framework that dynamically estimates a token budget suited to the complexity of various problems, thereby guiding the reasoning process towards a more cost-effective and efficient token usage.

Key Contributions

1. Identification of Token Redundancy: The research notes that current CoT techniques, which break down complex problems into intermediate steps, tend to produce unnecessarily lengthy outputs. This redundancy increases both the token cost and inference time, posing limitations in practical scenarios where resource optimization is critical.
2. Introduction of Token-Budget-Aware Reasoning Framework: The proposed framework strategically incorporates a token budget into the prompt, thus constraining the length of the reasoning process. This approach allows models to maintain high accuracy levels while significantly lowering the token output, as shown in multiple controlled experiments.
3. Dynamic Budget Estimation: At the core of the framework is a dynamic estimation mechanism that calculates an appropriate token budget based on the complexity of the task at hand. This estimation is crucial as it maintains the delicate balance between brevity and the accuracy of the model's output, ensuring that the quality of reasoning is not compromised even as token usage is optimized.

Experimental Findings

The experiments conducted reveal that with a token-budget-aware prompt, LLMs can achieve a substantial reduction in token usage—up to 68.64%—while suffering less than a 5% reduction in performance on various reasoning tasks. This indicates that a well-chosen token budget can effectively compress the reasoning process without significantly degrading the correctness of the responses, which was further demonstrated through practical scenarios on benchmark datasets like GSM8K.

Implications and Future Directions

The implications of this research are far-reaching within both theoretical and practical realms. By reducing the token overhead associated with reasoning, this approach not only makes LLM deployment more cost-efficient but also presents opportunities for usage in domains with stringent computational constraints. Moreover, the proposed dynamic budgeting mechanism lays the groundwork for advanced token management methods in AI, paving the way for more sophisticated LLM architectures that can self-regulate and optimize their reasoning processes.

Looking ahead, future research could explore integrating this framework with active learning strategies, allowing models to iteratively refine their token usage policies based on feedback or by utilizing reinforcement learning techniques to further enhance budget estimation accuracy. Additionally, applying token-budget-awareness to other reasoning frameworks and extending its applicability to diverse tasks would be a promising avenue for advancing this work.

Overall, this paper provides a robust foundation for enhancing the efficiency of LLMs and contributes novel insights into how prompting strategies can be innovatively manipulated to drive practical advancements in AI reasoning capabilities.