Rational Metareasoning for Large Language Models (2410.05563v3)

Abstract: Being prompted to engage in reasoning has emerged as a core technique for using LLMs, deploying additional inference-time compute to improve task performance. However, as LLMs increase in both size and adoption, inference costs are correspondingly becoming increasingly burdensome. How, then, might we optimize reasoning's cost-performance tradeoff? This work introduces a novel approach based on computational models of metareasoning used in cognitive science, training LLMs to selectively use intermediate reasoning steps only when necessary. We first develop a reward function that incorporates the Value of Computation by penalizing unnecessary reasoning, then use this reward function with Expert Iteration to train the LLM. Compared to few-shot chain-of-thought prompting and STaR, our method significantly reduces inference costs (20-37\% fewer tokens generated across three models) while maintaining task performance across diverse datasets.

• The paper introduces a rational metareasoning framework that adaptively selects inference paths to balance computational cost and performance.
• It employs a novel reward function based on the Value of Computation with Expert Iteration to optimize reasoning chains.
• Empirical results show a 20-37% reduction in token generation across diverse datasets while preserving robust task performance.

Rational Metareasoning for LLMs

The advent of LLMs has propelled the field of natural language processing, enabling significant advancements across multiple tasks. However, the computational demand associated with inference-time computations has become increasingly prominent. The paper "Rational Metareasoning for LLMs," authored by De Sabbata et al., addresses this challenge by introducing an approach inspired by human cognition, particularly the concept of rational metareasoning.

Overview

The authors propose a method to balance the trade-off between task performance and inference cost in LLMs. This is achieved by integrating a reward function that incorporates the Value of Computation (VOC), a concept rooted in rational metareasoning from cognitive science. The innovative aspect lies in training LLMs to engage in reasoning selectively, thereby using intermediate steps only when necessary. This is distinctly different from conventional techniques such as Chain-of-Thought (CoT) prompting, which are static and uniformly increase inference costs irrespective of task complexity.

The paper employs Expert Iteration in conjunction with the proposed reward function to fine-tune LLMs. By evaluating multiple reasoning chains per input and selecting the optimal one, the approach reduces unnecessary computations. Empirical results indicate a reduction in inference costs—20-37% fewer tokens generated across evaluated models—while maintaining performance across diverse datasets.

Methodological Contributions

1. Adaptive Reasoning via Rational Metareasoning: The paper pioneers the application of rational metareasoning in optimizing inference cost-performance for LLMs. This adaptive approach emulates human cognitive efficiency, selectively deploying reasoning based on task complexity.
2. Reward Function Based on VOC: The novel reward function developed incorporates both the utility of reasoning and the associated computational cost, trained via Expert Iteration. This enables models to autonomously balance task performance and cost dynamically during inference.
3. Empirical Validation Across Diverse Domains: The method was rigorously tested on various reasoning datasets, including science knowledge (ARC), commonsense reasoning (CommonsenseQA), mathematical problem solving (GSM8K), and logical deduction (ProofWriter). The substantial reduction in token generation achieved confirms the approach's effectiveness.

Implications and Future Directions

The implications of this work are significant for both practical applications and theoretical understandings:

• Practical Efficiency: By reducing inference costs without degrading performance, the approach is relevant for real-world LLM applications where resource constraints are a factor. It offers a pathway to more sustainable and cost-effective deployment of powerful models like GPT-3 and its successors.
• Robust Generalization: The findings indicating robust performance even on out-of-distribution tasks (as demonstrated on the MMLU benchmark) suggest that rational metareasoning can enhance the adaptability and generalization capacity of LLMs. This aspect is critical in dynamically changing environments and areas where pre-training data may be limited or non-representative.
• Theoretical Insights: By modeling LLM reasoning processes after human cognitive strategies, this research bridges AI and cognitive science, offering insights into how computational models can emulate intelligent human behavior. This perspective may inspire further interdisciplinary research, potentially leading to models that can effectively mimic nuanced human reasoning.

Future developments may involve extending this approach to enhance overall task performance, not merely efficiency. Additionally, exploring this model in specialized domains or integrating it with other machine learning paradigms can further validate and expand its utility.

In conclusion, the paper by De Sabbata et al. represents a thoughtful intersection of cognitive science and artificial intelligence, outlining a feasible strategy for rationalizing computation in LLMs. This work lays a robust foundation for ensuing research targeting efficiency and adaptiveness in computational reasoning.