Large language models (LLMs) go beyond basic natural language processing to solve complex problem-solving tasks. While expanding the scale, data and computing of the model enables the development of richer internal representations and emerging features in larger models, its inference capabilities are still challenging. Current methodologies are difficult to maintain coherence throughout complex problem solving, especially in areas where structured thinking is required. The difficulty lies in optimizing thoughtful reasoning and ensuring stable performance between various tasks, especially on challenging mathematical problems. Although recent advances have shown promise, researchers face the ongoing challenge of effectively utilizing computing resources to improve their inference capabilities without sacrificing efficiency. Developing ways to systematically enhance problem solving while maintaining scalability remains a central issue in improving LLM functionality.
Researchers explore various methods to enhance reasoning in LLM. The combination of test time computational scaling and reinforcement learning has become a promising direction, and the model uses inference tokens to guide the thought process. Research has investigated whether the model tends to overthink or think, thus examining the inference step size, input length, and common failure modes. Previous work focused on optimizing mathematical reasoning by conducting explicit thought-based training during the learning stage and iterative improvements in reasoning. Although these methods have shown improvements to the benchmark, there are still problems with the efficiency of token usage for different model functions and the relationship between inference length and performance. These questions are crucial to understanding how to design more efficient inference systems.
This study used the OMNI-MATH dataset to benchmark the inference ability across different model variants. This dataset provides a rigorous evaluation framework at the Olympian level to address the limitations of existing benchmarks such as GSM8K and math, while the current LLMS achieves high accuracy. The comprehensive organization of Omni-Math is divided into 33 subdomains at 10 difficulty levels, which allows for a subtle assessment of mathematical reasoning capabilities. The availability of Omni-Gudge helps to automatically evaluate the model-generated answers. Although other benchmarks such as MMLU, AI2 inference and GPQA cover different inference domains, and coding benchmarks emphasize the importance of explicit reward models, Omni-Math’s structure makes it particularly suitable for analyzing the relationship between inference length and performance between model capabilities across model functions.
The study evaluated model performance using the OMNI-MATH benchmark, which has 4,428 Olympic-level mathematical problems in six domains and four difficulty levels. The results show that the performance hierarchy between tested models is clear: GPT-4O achieves 20-30% accuracy between disciplines, significantly lagging behind the inference model. O1-Mini reaches 40-60%; O3-Mini(M) reaches at least 50% in all categories; O3-Mini(H) improves about 4% compared to O3-Mini(M), and the accuracy of algebra and calculus is over 80%. Token usage analysis shows that relative token consumption increases with the difficulty of problems in all models, and discrete math is particularly intensive. Importantly, O3-Mini(M) does not use more inference tokens than O1-Mini to achieve superior performance, thus proposing more efficient inference. Similarly, with the increase in token usage for all models, the accuracy decreases, with the strongest effect for O1-Mini (3.16% decrease per 1000 tokens), while O3-Mini(H)(H) is weakest (0.81% decrease). This shows that although O3-Mini(H) exhibits slightly performing performance, its computational cost is greatly improved.
This study produces two important findings on language model reasoning. First, more capable models do not necessarily require longer inference chains to achieve higher accuracy, as demonstrated by the comparison between O1-Mini and O3-Mini(M). Second, while accuracy usually decreases with longer thought chain processes, this effect decreases in more advanced models, emphasizing that “think harder” is different from “think longer thinking.” This decrease in accuracy may occur because models tend to reason more widely about problems they are difficult to solve, or because longer chains of reasoning essentially increase the likelihood of errors. These findings have practical implications for model deployment, suggesting that constrained thought chain length is more beneficial for weaker inference models than for powerful models, because the latter maintains reasonable accuracy even through extended inference. Future work may benefit from mathematical benchmarks with reference reasoning templates to further explore these dynamics.
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The post thinks harder, no longer: Evaluating the efficiency of reasoning in high-level language models first appeared on Marktechpost.
