13 citations · 36 across the 27 of their papers we have counts for
7 papers · 1 filter
Train4Merge: RL or SFT---Which Yields Better Teachers for OPD-Based Model Merging?
Jingyuan Huang, Zuming Huang, Yucheng Shi +4
Domain experts trained from a shared checkpoint can transfer their specialized capabilities to a single student through on-policy distillation (OPD). Existing research primarily fo…
OPERA: Aligning Open-Ended Reasoning via Objective Perplexity-based Reinforcement Learning
Wenxuan Jiang, Zining Fan, Zijian Zhang +6
Reinforcement Learning (RL) has enabled LLMs to excel in objective reasoning tasks such as mathematics and code generation. However, applying RL to open-ended tasks, such as creati…
SAERec: Constructing Fine-grained Interpretable Intents Priors via Sparse Autoencoders for Recommendation
Jiangnan Xia, Xuansheng Wu, Yu Yang +2
Intent-based recommender systems have gained significant attention for improving accuracy and interpretability by modeling the underlying motivations behind user behaviors. Most ex…
Trust the Right Teacher: Quality-Aware Self-Distillation for GUI Grounding
Jingyuan Huang, Zuming Huang, Yucheng Shi +4
GUI grounding requires vision-language models (VLMs) to identify small target elements in high-resolution screenshots and predict precise screen coordinates. On-policy self-distill…
Learnable Assessment Skills for LLM-based Automated Scoring: Rubric Construction via Iterative Optimization
Yun Wang, Xin Xia, Xuansheng Wu +2
LLM-based automated scoring approaches near-human performance, but scaling to new tasks remains bottlenecked by the per-item human configuration of upstream stages such as rubric c…
TR-ICRL: Test-Time Rethinking for In-Context Reinforcement Learning
Wenxuan Jiang, Yuxin Zuo, Zijian Zhang +8
In-Context Reinforcement Learning (ICRL) enables Large Language Models (LLMs) to learn online from external rewards directly within the context window. However, a central challenge…
BRIDGE the Gap: Mitigating Bias Amplification in Automated Scoring of English Language Learners via Inter-group Data Augmentation
Yun Wang, Xuansheng Wu, Jingyuan Huang +3
In the field of educational assessment, automated scoring systems increasingly rely on deep learning and large language models (LLMs). However, these systems face significant risks…