activity
20242026
most citedOmniGAIA: Towards Native Omni-Modal AI Agents

1 citations · 1 across the 3 of their papers we have counts for

collaborators

20 papers

cs.RO2026

Average-Power-Budgeted Underwater Vehicle Control via Constrained Reinforcement Learning

Yinuo Wang, Gavin Tao, Yuze Liu +1

Underwater vehicles operate from a fixed onboard energy budget that propulsion rapidly depletes, so a controller that completes its task while drawing less thruster power directly…

cs.AI20261 cited

OmniGAIA: Towards Native Omni-Modal AI Agents

Xiaoxi Li, Wenxiang Jiao, Jiarui Jin +10

Human intelligence naturally intertwines omni-modal perception -- spanning vision, audio, and language -- with complex reasoning and tool usage to interact with the world. However,…

cs.RO2026

Factor-Aware Mixture-of-Experts with Pretrained Encoder for Combinatorial Generalization

Feihong Zhang, Guojian Zhan, Zeyu He +8

The integration of pretrained encoders with diffusion policies has become a dominant paradigm for visual robotic manipulation. However, it still struggles to generalize across comp…

cs.CL2026

STAPO: Stabilizing Reinforcement Learning for LLMs by Silencing Rare Spurious Tokens

Shiqi Liu, Zeyu He, Guojian Zhan +10

Reinforcement Learning (RL) has significantly improved large language model reasoning, but existing RL fine-tuning methods rely heavily on heuristic techniques such as entropy regu…

cs.LG2026

Optimal Transport for LLM Reward Modeling from Noisy Preference

Licheng Pan, Haochen Yang, Haoxuan Li +8

Reward models are fundamental to Reinforcement Learning from Human Feedback (RLHF), yet real-world datasets are inevitably corrupted by noisy preference. Conventional training obje…

cs.CL2026

ImplicitRM: Unbiased Reward Modeling from Implicit Preference Data for LLM alignment

Hao Wang, Haocheng Yang, Licheng Pan +7

Reward modeling represents a long-standing challenge in reinforcement learning from human feedback (RLHF) for aligning language models. Current reward modeling is heavily contingen…