Phases of Uncertainty: Confidence–Calibration Dynamics in Language Model Training (2025)

Abstract: Autoregressive language models achieve strong performance across a wide range of natural language processing (NLP) tasks, yet their uncertainty estimates remain poorly understood, particularly during training. Prior work has primarily evaluated calibration and out-of-distribution (OOD) robustness at the final checkpoint, overlooking the dynamics that unfold earlier. We introduce a phase-based framework for tracking uncertainty metrics—including expected calibration error (ECE) and Kullback–Leibler (KL) divergence—across distinct stages of training. Using GPT-2 models trained across multiple random seeds, we find that uncertainty dynamics follow a consistent set of phases: models begin conservative and relatively well calibrated, but later phases introduce a paradoxical decoupling where confidence increases even as calibration worsens, especially under distribution shift. This paradox implies that the final checkpoint is not always the most reliable for deployment and motivates phase-aware strategies such as dynamic checkpoint selection or targeted calibration. Our findings highlight that uncertainty should be understood as a training-dependent property rather than a static one, opening new directions for scaling this framework to larger models, tasks, and distribution shift scenarios.

## introduction autoregressive language models have become central to a large portion of modern nlp, driving progress in tasks as varied as document summarization, dialogue, and code generation @xcite . yet, the impressive in-distribution performance of these models hides a recurring issue: their behavior is far less predictable when the input departs from the training distribution @xcite . in production settings, such outof-distribution (ood) cases are inevitable such as topic drift in conversational systems, domain mismatch in translation, or simply user queries that exploit corner cases in the model's learned representation. uncertainty estimation has become a way to address this problem. approaches such as bayesian approximations via dropout @xcite or calibration-based adjustments @xcite offer ways to associate model predictions with confidence scores. however, most of this work evaluates a final trained output. what is less well understood, especially in language modeling, is how uncertainty evolves during training itself. language models acquire the syntax, semantic, and task-specific reasoning in a staged manner, and their calibration profile is unlikely to be uniform across these stages @xcite . our key finding is that calibration does not improve monotonically with training: a mid-training phase emerges in which models grow more confident while becoming less calibrated. in this work, we introduce a phase-based framework for tracking and analyzing the joint dynamics of calibration error and kl divergence between successive stages of training. by segmenting model training into distinct phases and evaluating these metrics both in-distribution and ood, our approach offers a structured view of how and when models become more or less calibrated, and how their predictive distributions shift over time. ## conclusion we introduced a phase-based framework for analyzing uncertainty and calibration dynamics throughout language model training. across multiple seeds, we consistently observed a confidence-calibration paradox: models became less reliable precisely as their predictions grew more confident. this paradox was amplified under distribution shift, underscoring its practical importance for deployment safety. by framing uncertainty as a training-dependent property rather than a static one, we provide a foundation for phase-aware monitoring, checkpointing, and intervention strategies. in practice, our results motivate monitoring calibration (ece) jointly with validation loss, avoiding phase ii checkpoints when selecting release models, and applying simple posthoc calibration such as temperature scaling at deployment.