1 citations · 1 across the 3 of their papers we have counts for
5 papers
TextualRL: Textual Reinforcement Learning for Context Management
Tianyu Huai, Jie Zhou, Bo Mao +3
LLM agents can adapt to task feedback by revising the textual context that guides their behavior, without changing their model parameters. However, turning execution rewards into r…
Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction
AGI Team, Yuxuan Cai, Lu Chen +62
The evolution of Large Language Models (LLMs) from passive responders to autonomous agents necessitates a fundamental shift in learning paradigms -- from static imitation to incent…
Building Self-Evolving Agents via Experience-Driven Lifelong Learning: A Framework and Benchmark
Yuxuan Cai, Yipeng Hao, Jie Zhou +14
As AI advances toward general intelligence, the focus is shifting from systems optimized for static tasks to creating open-ended agents that learn continuously. In this paper, we i…
Task-Core Memory Management and Consolidation for Long-term Continual Learning
Tianyu Huai, Jie Zhou, Yuxuan Cai +5
In this paper, we focus on a long-term continual learning (CL) task, where a model learns sequentially from a stream of vast tasks over time, acquiring new knowledge while retainin…
Reinforced Interactive Continual Learning via Real-time Noisy Human Feedback
Yutao Yang, Jie Zhou, Junsong Li +5
This paper introduces an interactive continual learning paradigm where AI models dynamically learn new skills from real-time human feedback while retaining prior knowledge. This pa…