2 citations · 3 across the 26 of their papers we have counts for
4 papers · 1 filter
LatentLoop: Scaling Looped MoE Models with Asymmetric Compute Allocation
Zefeng He, Yunkai Chai, Xiaoyang Chen +5
Looped Mixture-of-Experts (MoE) models combine parameter reuse with sparse computation to improve performance under matched budgets. Prior work demonstrates that different loop vis…
ExGRPO: Learning to Reason from Experience
Runzhe Zhan, Yafu Li, Zhi Wang +5
Reinforcement learning from verifiable rewards (RLVR) is an emerging paradigm for improving the reasoning ability of large language models. However, standard on-policy training dis…
Rethinking Entropy Regularization in Large Reasoning Models
Yuxian Jiang, Yafu Li, Guanxu Chen +3
Reinforcement learning with verifiable rewards (RLVR) has shown great promise in enhancing the reasoning abilities of large reasoning models (LRMs). However, it suffers from a crit…
Advancing Multimodal Reasoning: From Optimized Cold Start to Staged Reinforcement Learning
Shuang Chen, Yue Guo, Zhaochen Su +7
Inspired by the remarkable reasoning capabilities of Deepseek-R1 in complex textual tasks, many works attempt to incentivize similar capabilities in Multimodal Large Language Model…