This article investigates the effectiveness of task-level pseudocode in improving logical reasoning within code learning models, specifically through the THINK-AND-EXECUTE method. Experiments reveal that task-level pseudocode is superior to instance-specific pseudocode, yielding approximately double the performance improvements. Additionally, pre-training on extensive code datasets markedly enhances the reasoning abilities of models, with results demonstrating that CodeLlama-13B outperforms Llama-13B across various tasks. The implications suggest a need for further exploration in pseudocode application and the influence of pre-training on code logic understanding.
The use of task-level pseudocode dramatically enhances performance, resulting in THINK-AND-EXECUTE achieving about twice the effectiveness of Chain-of-Code in logical reasoning tasks.
Findings illustrate that pre-training on code corpora significantly improves the reasoning capabilities of models like CodeLlama-13B compared to Llama-13B when using THINK-AND-EXECUTE.
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