1 citations · 1 across the 11 of their papers we have counts for
18 papers
Reuse your FLOPs: Scaling RL on Hard Problems by Conditioning on Very Off-Policy Prefixes
Amrith Setlur, Zijian Wang, Andrew Cohen +2
Typical reinforcement learning (RL) methods for LLM reasoning waste compute on hard problems, where correct on-policy traces are rare, policy gradients vanish, and learning stalls.…
PaperGuide: Making Small Language-Model Paper-Reading Agents More Efficient
Zijian Wang, Tiancheng Huang, Hanqi Li +3
The accelerating growth of the scientific literature makes it increasingly difficult for researchers to track new advances through manual reading alone. Recent progress in large la…
ThinkDrive: Chain-of-Thought Guided Progressive Reinforcement Learning Fine-Tuning for Autonomous Driving
Chang Zhao, Zheming Yang, Yunqing Hu +4
With the rapid advancement of large language models (LLMs) technologies, their application in the domain of autonomous driving has become increasingly widespread. However, existing…
Affordance Field Intervention: Enabling VLAs to Escape Memory Traps in Robotic Manipulation
Siyu Xu, Zijian Wang, Yunke Wang +3
Vision-Language-Action (VLA) models have shown great performance in robotic manipulation by mapping visual observations and language instructions directly to actions. However, they…
Eliciting Chain-of-Thought in Base LLMs via Gradient-Based Representation Optimization
Zijian Wang, Yanxiang Ma, Chang Xu
Chain-of-Thought (CoT) reasoning is a critical capability for large language models (LLMs), enabling them to tackle com- plex multi-step tasks. While base LLMs, pre-trained on gene…
Controllable Flow Matching for Online Reinforcement Learning
Bin Wang, Boxiang Tao, Haifeng Jing +2
Model-based reinforcement learning (MBRL) typically relies on modeling environment dynamics for data efficiency. However, due to the accumulation of model errors over long-horizon…