Research on post-training large language models has largely overlooked whether complete reasoning traces—long trajectories that include detours toward an answer—are actually necessary, according to a study titled "Revisiting Complete Reasoning Traces for Post-Training." The work examines how LLMs are typically post-trained on pre-collected reasoning trajectories to improve reasoning capability, noting those trajectories tend to be long due to complex, interwoven paths. The source provides no named authors, institutions, or quantitative results.
Large language models (LLMs) are often post-trained on pre-collected reasoning trajectories to improve their reasoning capability. Such trajectories tend to be long due to complex, interwoven paths, which often include detours on the path toward the answer. However, it has been underexplored whether
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