4 papers
Context and Diversity Matter: The Emergence of In-Context Learning in World Models
Fan Wang, Zhiyuan Chen, Yuxuan Zhong +8
The capability of predicting environmental dynamics underpins both biological neural systems and general embodied AI in adapting to their surroundings. Yet prevailing approaches re…
IIB-LPO: Latent Policy Optimization via Iterative Information Bottleneck
Huilin Deng, Hongchen Luo, Yue Zhu +8
Recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) for Large Language Model (LLM) reasoning have been hindered by a persistent challenge: exploration collapse…
Towards Large-Scale In-Context Reinforcement Learning by Meta-Training in Randomized Worlds
Fan Wang, Pengtao Shao, Yiming Zhang +6
In-Context Reinforcement Learning (ICRL) enables agents to learn automatically and on-the-fly from their interactive experiences. However, a major challenge in scaling up ICRL is t…
Boosting the Generalization and Reasoning of Vision Language Models with Curriculum Reinforcement Learning
Huilin Deng, Ding Zou, Rui Ma +3
While state-of-the-art vision-language models (VLMs) have demonstrated remarkable capabilities in complex visual-text tasks, their success heavily relies on massive model scaling,…