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This paper introduces KGPS, a Kalman-guided prompt selection method for adaptive RL finetuning of LLMs, which models prompt difficulty as a dynamic state to improve accuracy and rollout efficiency.
Introduces the Data-Model Compatibility (DMC) metric to evaluate how well a reasoning dataset aligns with a student model during distillation. Experiments show DMC strongly correlates with distillation performance and that dynamically selecting datasets based on DMC further improves reasoning capabilities.