1 citations · 1 across the 3 of their papers we have counts for
5 papers
Learning from Interaction Experience via Localized Context Distillation
Zhezheng Hao, Yongkang Yang, Xilin Xia +9
Learning from interaction experience is essential for enabling large language models to continually improve during deployment. Context distillation offers a promising path: privile…
ImproveAnyTask: An Autonomous Post-Training Harness for Iterative Model Self-Improvement
Xingbo Yao, Xiaoman Wang, Zhengwu Lei +11
Adapting general-purpose large language models to specific tasks requires substantial human effort in designing data and training strategies. Sustaining improvement is especially c…
Unsupervised Post-Training of Foundation Models: A Survey
Yijie Xu, Qianyi Cai, Huizai Yao +9
Foundation-model post-training usually relies on human labels, preference data, stronger teachers, or executable verifiers. We study Unsupervised Post-Training (UPT): update-bearin…
HumanLLM: Towards Personalized Understanding and Simulation of Human Nature
Yuxuan Lei, Tianfu Wang, Jianxun Lian +3
Motivated by the remarkable progress of large language models (LLMs) in objective tasks like mathematics and coding, there is growing interest in their potential to simulate human…
Anti-Length Shift: Dynamic Outlier Truncation for Training Efficient Reasoning Models
Wei Wu, Liyi Chen, Congxi Xiao +7
Large reasoning models enhanced by reinforcement learning with verifiable rewards have achieved significant performance gains by extending their chain-of-thought. However, this par…