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ForGE is introduced, a failure-guided framework that co-evolves prompts and training data and establishes failures as a shared interface between prompt optimization and data synthesis, and shows the benefit of jointly adapting what a model is instructed to do and what it learns from
Large language models have made strong reasoning gains through supervised fine-tuning, reinforcement learning, and on-policy distillation, yet these post-training methods are usually evaluated only by final-answer accuracy. We study how they reshape confidence during reasoning. We introduce a three-stage calibration fr...
A three-stage calibration framework that evaluates confidence before, during, and after chain-of-thought generation, corresponding to difficulty estimation, early termination, and answer aggregation finds that OPD provides the most useful pre-reasoning confidence, SFT gives the strongest online signal for early stoppin...
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