Brecht Verbeken

Abstract Fine-tuning Large Language Models (LLMs) with reinforcement learning to produce explicit Chain-of-Thought (CoT) reasoning improves performance across code, math, and general-knowledge benchmarks. However, on tasks where accuracy remains low, such as Humanity’s Last Exam (HLE), models often express high self-confidence despite incorrect answers, revealing poor calibration. We investigate whether measurable properties of the CoT offer reliable, model-internal confidence signals.