5 papers · 1 filter
IRDS: Interpretable RLVR Data Selection via Verifier-Coupled Sparse Autoencoder Coverage
Yuhan Li, Mingxu Zhang, Dazhong Shen +1
Reinforcement learning with verifiable rewards (RLVR) has become a key technique for en- hancing LLM reasoning, yet its data ineffi- ciency remains a major bottleneck. Existing met…
SAE-FD: Sparse Autoencoder Feature Distillation for Continual Learning of Large Language Models
Mingxu Zhang, Yuhan Li, Lujundong Li +3
Continual learning enables large language models to adapt to evolving tasks without retraining from scratch, yet catastrophic forgetting remains a central obstacle. Among continual…
SLIM: Sparse Latent Steering for Interpretable and Property-Directed LLM-Based Molecular Editing
Mingxu Zhang, Yuhan Li, Lujundong Li +3
Large language models possess strong chemical reasoning capabilities, making them effective molecular editors. However, property-relevant information is implicitly entangled across…
NGTM: Substructure-based Neural Graph Topic Model for Interpretable Graph Generation
Yuanxin Zhuang, Dazhong Shen, Ying Sun
Graph generation plays a pivotal role across numerous domains, including molecular design and knowledge graph construction. Although existing methods achieve considerable success i…
MolEditRL: Structure-Preserving Molecular Editing via Discrete Diffusion and Reinforcement Learning
Yuanxin Zhuang, Dazhong Shen, Ying Sun
Molecular editing aims to modify a given molecule to optimize desired chemical properties while preserving structural similarity. However, current approaches typically rely on stri…