Papers by Zixiong Yu

2 papers
Dynamic Sampling that Adapts: Self-Aware Iterative Data Persistent Optimization for Mathematical Reasoning (2026.findings-acl)

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Challenge: Current data selection paradigms rely on static, externally defined metrics, which fail to adapt to the evolving capabilities of models during training.
Approach: They propose a dynamic sampling framework that aligns training data with the model's intrinsic competence by iterating on real-time feedback.
Outcome: Extensive experiments on eight benchmarks show that SAI-DPO outperforms static baselines at most nearly 6 points, achieving state-of-the-art efficiency with significantly less data.
MathAgent: Adversarial Evolution of Constraint Graphs for Mathematical Reasoning Data Synthesis (2026.findings-acl)

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Challenge: Current approaches to synthesising high-quality mathematical reasoning data without human priors suffer from mode collapse and limited logical complexity.
Approach: They propose a hierarchical synthesis framework that formulates data synthesis as an unsupervised optimization problem over a constraint graph followed by semantic instantiation rather than a direct text generation task.
Outcome: The proposed framework outperforms widely-used datasets on eight mathematical benchmarks.

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