When Reasoning Meets Its Laws
Despite the superior performance of Large Reasoning Models (LRMs), their reasoning behaviors are often counterintuitive, leading to suboptimal reasoning capabilities. To theoretically formalize the desired reasoning behaviors, this paper presents the Laws of Reasoning (LoRe), a unified framework that characterizes intrinsic reasoning patterns in LRMs. We first propose compute law with the hypothesis that the reasoning compute should scale linearly with question complexity. Beyond compute, we extend LoRe with a supplementary accuracy law. Since the question complexity is difficult to quantify in practice, we examine these hypotheses by two properties of the laws, monotonicity and compositionality. We therefore introduce LoRe-Bench, a benchmark that systematically measures these two tractable properties for large reasoning models. Evaluation shows that most reasoning models exhibit reasonable monotonicity but lack compositionality. In response, we develop an effective finetuning approach that enforces compute-law compositionality. Extensive empirical studies demonstrate that better compliance with compute laws yields consistently improved reasoning performance on multiple benchmarks, and uncovers synergistic effects across properties and laws. Project page: https://lore-project.github.io/
6948af7534f46eaf46cbb1da
Junyu Zhang
claimed_verified
2025-12-22T10:58:53.009Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/6719bfd07c6e6c83a388aeae/jHxryk04dzHo23TX5F5sz.png
Junyu Zhang
jyzhang1208
user
jyzhang1208
6948af7534f46eaf46cbb1db
Yifan Sun
6948af7534f46eaf46cbb1dc
Tianang Leng
claimed_verified
2025-12-25T20:52:16.542Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/65767cf9cbd807a02e02abe1/P1zuBjyo0a8EgmkhhhRLv.jpeg
Tianang Leng
Kiria-Nozan
user
Kiria-Nozan
6948af7534f46eaf46cbb1dd
Jingyan Shen
6948af7534f46eaf46cbb1de
Liu Ziyin
6948af7534f46eaf46cbb1df
Paul Pu Liang
6948af7534f46eaf46cbb1e0
Huan Zhang
When Reasoning Meets Its Laws
Despite the superior performance of Large Reasoning Models (LRMs), their reasoning behaviors are often counterintuitive, leading to suboptimal reasoning capabilities. To theoretically formalize the desired reasoning behaviors, this paper presents the Laws of Reasoning (LoRe), a unified framework that characterizes intrinsic reasoning patterns in LRMs. We first propose compute law with the hypothesis that the reasoning compute should scale linearly with question complexity. Beyond compute, we extend LoRe with a supplementary accuracy law. Since the question complexity is difficult to quantify in practice, we examine these hypotheses by two properties of the laws, monotonicity and compositionality. We therefore introduce LoRe-Bench, a benchmark that systematically measures these two tractable properties for large reasoning models. Evaluation shows that most reasoning models exhibit reasonable monotonicity but lack compositionality. In response, we develop an effective finetuning approach that enforces compute-law compositionality. Extensive empirical studies demonstrate that better compliance with compute laws yields consistently improved reasoning performance on multiple benchmarks, and uncovers synergistic effects across properties and laws. Project page: https://lore-project.github.io/
A framework called Laws of Reasoning (LoRe) is introduced to theoretically define desired reasoning behaviors in Large Reasoning Models, with a focus on compute and accuracy laws, and a benchmark (LoRe-Bench) to measure these properties.
Laws of Reasoning LoRe compute law accuracy law question complexity monotonicity compositionality LoRe-Bench finetuning approach compute-law compositionality