11 papers
Rethinking Transfer in Continual Learning: A Replay-Based Realisation
Yang Meng, Zhenya Liu, Zhuokai Zhao +1
Continual learning studies how deployed language models can continually acquire new tasks without expensive retraining from scratch. Existing methods, whether rehearsal-based (repl…
Accelerating PDE Surrogates via RL-Guided Mesh Optimization
Yang Meng, Ruoxi Jiang, Zhuokai Zhao +3
Deep surrogate models for parametric partial differential equations (PDEs) can deliver high-fidelity approximations but remain prohibitively data-hungry: training often requires th…
Enhancing Vision-Language Model Reliability with Uncertainty-Guided Dropout Decoding
Yixiong Fang, Ziran Yang, Zhaorun Chen +2
Large vision-language models (LVLMs) excel at multimodal tasks but are prone to misinterpreting visual inputs, often resulting in hallucinations and unreliable outputs. We present…
Scaling Agent Learning via Experience Synthesis
Zhaorun Chen, Zhuokai Zhao, Kai Zhang +15
While reinforcement learning (RL) can empower autonomous agents by enabling self-improvement through interaction, its practical adoption remains challenging due to costly rollouts,…
DISCO Balances the Scales: Adaptive Domain- and Difficulty-Aware Reinforcement Learning on Imbalanced Data
Yuhang Zhou, Jing Zhu, Shengyi Qian +7
Large Language Models (LLMs) are increasingly aligned with human preferences through Reinforcement Learning from Human Feedback (RLHF). Among RLHF methods, Group Relative Policy Op…
Boosting LLM Reasoning via Spontaneous Self-Correction
Xutong Zhao, Tengyu Xu, Xuewei Wang +11
While large language models (LLMs) have demonstrated remarkable success on a broad range of tasks, math reasoning remains a challenging one. One of the approaches for improving mat…