Papers by Guhao Feng

2 papers
Beyond Online Sampling: Bridging Offline-to-Online Alignment via Dynamic Data Transformation for LLMs (2025.emnlp-main)

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Challenge: Direct Preference Optimization (DPO) eliminates complex reward modeling in aligning large language models with human preferences, but its online variant faces significant efficiency bottlenecks due to costly real-time preference sampling and the reward model annotation.
Approach: They propose a framework that transforms static datasets into dynamically adaptive equivalents without the need for an explicit reward model.
Outcome: The proposed approach matches or exceeds the performance of a fully online DPO.
How Numerical Precision Affects Arithmetical Reasoning Capabilities of LLMs (2025.findings-acl)

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Challenge: Despite the success of transformer-based large language models, understanding and enhancing their mathematical capabilities remains a significant challenge.
Approach: They propose to use numerical precision as a key factor that influences LLMs' effectiveness in arithmetical tasks to determine their effectiveness.
Outcome: The proposed models perform better in arithmetic tasks than transformer-based models with standard numerical precision.

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