Large language models (LLMs) are rapidly advancing, and researchers are constantly seeking ways to improve their performance. A promising technique is test-time scaling (TTS), which involves generating multiple reasoning traces and selecting the best one. Recent research, detailed in a new arXiv paper (arXiv:2510.02611), delves into the limitations of traditional TTS and introduces an innovative approach: scaling along the temperature dimension. This novel method allows us to further enhance these powerful models.
The Limits of Sample Scaling
Previous studies have established that increasing the number of samples (K) during test-time scaling (TTS) generally leads to improved accuracy. However, this paper reveals a surprising finding: the benefits of increased sampling eventually plateau. Beyond a certain point, adding more traces doesn’t yield further gains and some challenging questions remain unsolved regardless of the number of attempts. Therefore, simply scaling up the sample count isn’t always the most effective strategy for improving LLM performance; there are diminishing returns.
Why Temperature Matters
The research highlights a key observation: different sampling temperatures excel at solving distinct subsets of problems. Single-temperature TTS, consequently, only explores a portion of the model’s potential reasoning capabilities. This realization led researchers to investigate scaling along the temperature dimension – essentially exploring how varying the randomness in the model’s output affects performance. Furthermore, it demonstrates that incorporating temperature variability can unlock hidden abilities within the LLM.
Temperature Scaling: A New Approach
The team proposes and evaluates this new technique, demonstrating its effectiveness across various models (Qwen3 – 0.6B, 1.7B, 4B, and 8B) and five reasoning benchmarks including AIME 2024/2025, MATH500, LiveCodeBench, and Hi-ToM. The results are compelling: temperature scaling boosted performance by an average of 7.3 points compared to single-temperature TTS. Notably, this technique allowed base models to achieve performance levels comparable to those trained using reinforcement learning (RL), all without requiring additional post-training. In addition, a multi-temperature voting method was developed to mitigate the computational overhead associated with exploring multiple temperatures, allowing for efficient harnessing of temperature scaling’s benefits.
Conclusion: Unlocking Latent Potential
This study underscores that test-time scaling possesses greater potential than previously recognized. By incorporating temperature scaling, we can effectively unlock the latent reasoning abilities within base LLMs, achieving significant performance improvements and potentially eliminating the need for resource-intensive RL training. This represents a valuable advancement in optimizing large language models and expanding their capabilities. Ultimately, this offers an accessible way to improve LLM functionality using temperature scaling without extensive retraining.
Source: Read the original article here.
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