From the 1 of 8 linked papers with an AI index.
8 papers
SkillZip: Evaluation-Free Skill Compression for Self-Evolving Agents by Discovering Reusable Structure
Xiaofan Bai, Hongqiang Lin, Chao Liu +4
Self-evolving agents accumulate reusable skills by appending successful procedures and failure fixes. Over time, the same requirement is often restated in several branches, example…
Rethinking Self-Evolution: A Constrained Exploration-Exploitation Process for Mitigating Skill Overfitting
Hongqiang Lin, Chao Liu, Xiaofan Bai +4
The paper introduces SkillBoost, a three-stage framework that reduces overfitting of trainable skills in large language model agents by balancing constrained exploitation of failur…
Robust Regularized Policy Iteration under Transition Uncertainty
Hongqiang Lin, Zhenghui Fu, Weihao Tang +4
Offline reinforcement learning (RL) enables data-efficient and safe policy learning without online exploration, but its performance often degrades under distribution shift. The lea…
Regularized Offline Policy Optimization with Posterior Hybrid Bayesian Belief
Hongqiang Lin, Pengfei Wang, Nenggan Zheng
Offline reinforcement learning (RL) aims to optimize policies from pre-collected datasets. A bottleneck of this paradigm is managing epistemic uncertainty, which arises from limite…
Offline Policy Optimization with Posterior Sampling
Hongqiang Lin, Dongxu Zhang, Yiding Sun +3
A fundamental challenge in model-based offline reinforcement learning (RL) lies in the trade-off between generalization and robustness against exploitation errors in out-of-distrib…
CFMS: A Coarse-to-Fine Multimodal Synthesis Framework for Enhanced Tabular Reasoning
Qixian Huang, Hongqiang Lin, Tong Fu +5
Reasoning over tabular data is a crucial capability for tasks like question answering and fact verification, as it requires models to comprehend both free-form questions and semi-s…