On the Overthinking of LLMs

Key points

The problem is that o1-family models often spend long reasoning traces on simple tasks, which brings only limited accuracy gains while increasing latency and cost.
In evaluation, the paper proposes outcome-level and process-level efficiency metrics to measure whether reasoning compute is being used sensibly.
The method trains the model with a self-learning strategy to simplify reasoning while preserving answer quality across tasks of different difficulty.
The implication is that future reasoning systems need adaptive test-time compute policies, not a habit of using verbose chains of thought for every query.

External research summaries. These are not HDATF publications or measured product results.