Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies Ahead

2025-03-31

AI Summary

This work systematically investigates the efficacy and fundamental limits of inference-time scaling for enhancing large language models’ reasoning capabilities on complex tasks. Method: We evaluate eight challenging non-mathematical domains—including mathematical/STEM reasoning, calendar scheduling, NP-hard problems, navigation, and spatial reasoning—using nine state-of-the-art models (e.g., GPT-4o, o1), multiple inference paradigms (independent calls vs. feedback-augmented chain-of-calls), and a rigorous evaluation framework incorporating perfect verifiers. Contribution/Results: We establish that reasoning gains diminish significantly with increasing task complexity; token budget expansion does not monotonically improve accuracy and exhibits well-defined upper and lower performance bounds; robust feedback mechanisms unlock substantial headroom across all models; and, notably, standard models augmented with perfect verifiers achieve average performance comparable to advanced reasoning models on several tasks—revealing verifiability as a critical bottleneck and scalability lever.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Inference-time scaling can enhance the reasoning capabilities of large language models (LLMs) on complex problems that benefit from step-by-step problem solving. Although lengthening generated scratchpads has proven effective for mathematical tasks, the broader impact of this approach on other tasks remains less clear. In this work, we investigate the benefits and limitations of scaling methods across nine state-of-the-art models and eight challenging tasks, including math and STEM reasoning, calendar planning, NP-hard problems, navigation, and spatial reasoning. We compare conventional models (e.g., GPT-4o) with models fine-tuned for inference-time scaling (e.g., o1) through evaluation protocols that involve repeated model calls, either independently or sequentially with feedback. These evaluations approximate lower and upper performance bounds and potential for future performance improvements for each model, whether through enhanced training or multi-model inference systems. Our extensive empirical analysis reveals that the advantages of inference-time scaling vary across tasks and diminish as problem complexity increases. In addition, simply using more tokens does not necessarily translate to higher accuracy in these challenging regimes. Results from multiple independent runs with conventional models using perfect verifiers show that, for some tasks, these models can achieve performance close to the average performance of today's most advanced reasoning models. However, for other tasks, a significant performance gap remains, even in very high scaling regimes. Encouragingly, all models demonstrate significant gains when inference is further scaled with perfect verifiers or strong feedback, suggesting ample potential for future improvements.

Problem

• Investigates benefits and limitations of inference-time scaling on complex tasks
• Compares conventional and fine-tuned models across diverse challenging tasks
• Evaluates performance bounds and future potential of scaling methods

Innovation

• Investigates inference-time scaling across multiple models
• Compares conventional and fine-tuned models with feedback
• Reveals task-dependent benefits of scaling methods