arXiv Machine Learning By Haoyang He, Zihua Rong, Yunjia Zhao, Lan Yang, Jian Chang, Honggang Zhang

TTSR: Test-Time Self-Evolving via Reflection

Read the original on arXiv Machine Learning →

TTSR (Test-Time Self-Reflection) is a framework that enables large language models to adapt during inference by alternating between a Student role that solves test questions and a Teacher role that analyzes failures and generates targeted variant questions. The method incorporates a weakness memory and a strategy note to guide exploration, reducing reliance on noisy pseudo-labels and inefficient rollouts. Experiments on mathematical reasoning benchmarks demonstrate consistent test-time improvements, strong cross-backbone generalization, and transfer to general-domain reasoning tasks.

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