Assess the generality and optimization effects of CAST

Determine how CAST’s uncertainty–smoothness credit allocation affects full-model optimization and training collapse across broader model families and datasets under matched-compute evaluation.

Background

The paper evaluates CAST primarily on Qwen3-8B distillation into Mistral-7B and Llama-3.1-8B, using reasoning-oriented datasets and training configurations whose compute budgets vary across methods. Although the reported experiments include ablations and several training seeds, the authors note that these results do not establish how the method behaves across a substantially broader range of models and data distributions.

The unresolved future work concerns both external validity and mechanism: broader model and data coverage and matched-compute comparisons are needed, together with direct experiments testing how CAST’s token-level credit allocation influences full-model optimization dynamics and the incidence of training collapse.