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Introduces a curation-free metric (Triangulated Preference Shift) to isolate and quantify lexical biases induced during preference learning in LLMs, without manual curation, across six model families.
The paper proposes High-Entropy Sum (HES), a training-free metric for selecting high-quality reasoning data for LLM training, validated across SFT, RFT, and RL paradigms.
This paper introduces the Spectral Energy Centroid (SEC) metric to analyze and improve spectral bias in implicit neural representations, demonstrating its utility for hyperparameter selection, signal complexity measurement, and cross-architecture alignment.