Papers by Zhiguo Yang

2 papers
Bridging SFT and RL: Dynamic Policy Optimization for Robust Reasoning (2026.findings-acl)

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Challenge: Existing unified optimization strategies overlook the statistical conflict between these distinct gradient signals.
Approach: They propose a framework to reduce bias-variance trade-offs in Large Language Models . they propose DYPO, which leverages intrinsic group dynamics to significantly reduce RL gradient variance .
Outcome: The proposed framework outperforms traditional pipelines on reasoning benchmarks and out-of-distribution tasks.
Global Context or Local Detail? Adaptive Visual Grounding for Hallucination Mitigation (2026.findings-acl)

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Challenge: Large vision–language models suffer from object-existence hallucinations when multi-step deliberation decouples from visual evidence.
Approach: They propose a framework that allocates visual computation by uncertainty . they propose highlighting retains global context, while selective zoom-in performs local verification.
Outcome: The proposed framework reduces the complexity of multimodal reasoning by minimizing the operator trade-off.

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