In reasoning models, 'overthinking' refers to a phenomenon where models tend to explore incorrect alternatives after identifying the correct solution, leading to inefficiencies in the reasoning process. The source states that in simpler problems, reasoning models often find the correct solutions early but then continue to explore incorrect solutions, which wastes computational resources. This “overthinking” results in suboptimal performance as the models fail to maximize efficiency in their thought processes. As problem complexity increases, the models initially identify correct solutions later in their thinking, largely after extensive exploration of incorrect paths, further illustrating the challenges presented by overthinking in reasoning contexts[1].
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