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Weijia Xu

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2 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Learning to Solve Complex Problems via Dataset Decomposition

Wanru Zhao
Lucas Page-Caccia
Zhengyan Shi
Minseon Kim
Weijia Xu
Alessandro Sordoni

Curriculum learning is a class of training strategies that organizes the data being exposed to a model by difficulty, gradually from simpler to more complex examples. This research explores a reverse curriculum generation approach that recursively decomposes complex datasets into simpler, more learnable components. We propose a teacher-student framework where the teacher is equipped with the ability to reason step-by-step, which is used to recursively generate easier versions of examples, enabling the student model to progressively master difficult tasks. We propose a novel scoring system to measure data difficulty based on its structural complexity and conceptual depth, allowing curriculum construction over decomposed data. Experiments on math datasets (MATH and AIME) and code generation datasets demonstrate that models trained with curricula generated by our approach exhibit superior performance compared to standard training on original datasets.

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ICML Conference 2024 Conference Paper

Reprompting: Automated Chain-of-Thought Prompt Inference Through Gibbs Sampling

Weijia Xu
Andrzej Banburski
Nebojsa Jojic

We introduce Reprompting, an iterative sampling algorithm that automatically learns the Chain-of-Thought (CoT) recipes for a given task without human intervention. Through Gibbs sampling, Reprompting infers the CoT recipes that work consistently well for a set of training samples by iteratively sampling new recipes using previously sampled recipes as parent prompts to solve other training problems. We conduct extensive experiments on 20 challenging reasoning tasks. Results show that Reprompting outperforms human-written CoT prompts substantially by +9. 4 points on average. It also achieves consistently better performance than the state-of-the-art prompt optimization and decoding algorithms.

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