1 citations · 2 across the 9 of their papers we have counts for
9 papers
RoboDrop: Curating VLA Post-Training Data via Local Gradient Compatibility
Runze Xu, Yuanfan Xu, Cuijie Xu +4
Vision--language--action (VLA) models acquire broad generalization through large-scale pretraining, yet adapting them to a new task and robot embodiment still requires post-trainin…
On-policy Distillation with Verifiable Reward
Wenze Lin, Jiale Zhao, Xitai Jiang +5
Reinforcement Learning with Verifiable Rewards (RLVR) and on-policy distillation (OPD) have become two widely adopted paradigms for post-training large language models. However, RL…
Dual Attention Residuals
Xingda Yu, Yining Li, Xinzhang Liu +5
Recent work extends Transformer residual pathways along two complementary axes: historical retrieval selects information from earlier depths, whereas multi-stream methods maintain…
Provable Last-Iterate Convergence for Multi-Objective Safe LLM Alignment via Optimistic Primal-Dual
Yining Li, Peizhong Ju, Ness Shroff
Reinforcement Learning from Human Feedback (RLHF) plays a significant role in aligning Large Language Models (LLMs) with human preferences. While RLHF with expected reward constrai…
RouteMoA: Dynamic Routing without Pre-Inference Boosts Efficient Mixture-of-Agents
Jize Wang, Han Wu, Zhiyuan You +9
Mixture-of-Agents (MoA) improves LLM performance through layered collaboration, but its dense topology raises costs and latency. Existing methods employ LLM judges to filter respon…
ViT: Unlocking Test-Time Training in Vision
Dongchen Han, Yining Li, Tianyu Li +6
Test-Time Training (TTT) has recently emerged as a promising direction for efficient sequence modeling. TTT reformulates attention operation as an online learning problem, construc…
How to Find the Exact Pareto Front for Multi-Objective MDPs?
Yining Li, Peizhong Ju, Ness B. Shroff
Multi-Objective Markov Decision Processes (MO-MDPs) are receiving increasing attention, as real-world decision-making problems often involve conflicting objectives that cannot be a…
Achieving Sample and Computational Efficient Reinforcement Learning by Action Space Reduction via Grouping
Yining Li, Peizhong Ju, Ness Shroff
Reinforcement learning often needs to deal with the exponential growth of states and actions when exploring optimal control in high-dimensional spaces (often known as the curse of…
DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization
Peiwen Qiu, Yining Li, Zhuqing Liu +5
Decentralized bilevel optimization has received increasing attention recently due to its foundational role in many emerging multi-agent learning paradigms (e.g., multi-agent meta-l…