Papers by Bruce Qin

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Challenge:	Existing evaluators of large language models are static and lack the ability to refine their reasoning through interaction.
Approach:	They propose an Alpha–Beta Learning framework that trains two complementary 8B models: an Alpha classifier that assesses pairwise story engagement, and a Beta generator that produces structured, rubric-guided comparative explanations.
Outcome:	The proposed framework outperforms strong single-model baselines on human-annotated story-pair datasets in both accuracy and explanation quality across multiple iterative rounds.