26 citations · 27 across the 5 of their papers we have counts for
5 papers
Perceive, Interact, Reason: Building Tool-Augmented Visual Agents for Spatial Reasoning
Changye Li, Meng Lu, Yi Wu +1
While recent vision-language models (VLMs) demonstrate strong multimodal understanding, they remain limited in spatial reasoning tasks that require active evidence acquisition and…
Verifiable Process Rewards for Agentic Reasoning
Huining Yuan, Zelai Xu, Huaijie Wang +6
Reinforcement learning from verifiable rewards (RLVR) has improved the reasoning capabilities of large language models (LLMs), yet existing approaches predominantly rely on sparse…
HTPO: Towards Exploration-Exploitation Balanced Policy Optimization via Hierarchical Token-level Objective Control
Xincheng Yao, Ruoqi Li, Cheng Chen +4
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a pivotal technique for enhancing the reasoning capabilities of Large Language Models (LLMs). However, the de f…
Offline Reinforcement Learning for LLM Multi-Step Reasoning
Huaijie Wang, Shibo Hao, Hanze Dong +4
Improving the multi-step reasoning ability of large language models (LLMs) with offline reinforcement learning (RL) is essential for quickly adapting them to complex tasks. While D…
BitNet: Scaling 1-bit Transformers for Large Language Models
Hongyu Wang, Shuming Ma, Li Dong +7
The increasing size of large language models has posed challenges for deployment and raised concerns about environmental impact due to high energy consumption. In this work, we int…