Chenliang Li

State2State: Environment-Derived Mid-Training for LLM Agents

Xuanyu Lei, Yiqi Zhu, Chenliang Li, et al.

R2-Write: Reflection and Revision for Open-Ended Writing with Deep Reasoning

Wanlong Liu, Bo Zhang, Chenliang Li, et al.

Enhancing Pretrained Model-based Continual Representation Learning via Guided Random Projection

Ruilin Li, Heming Zou, Xiufeng Yan, et al.

This paper addresses a problem in continual learning (where AI models learn new tasks sequentially without forgetting old ones) by improving how randomly initialized projection layers work with pre-trained models. The authors propose SCL-MGSM, a method that intelligently selects which random features to use rather than using purely random ones, making the system more stable and accurate when learning new tasks with limited examples.