1 citations · 1 across the 6 of their papers we have counts for
7 papers
GaugeVLM: Structuring Spatial Supervision with Measured Geometric Interventions
Hongbo Wang, Zihan Lin, Wenkui Yang +5
Vision-language models (VLMs) can contradict themselves across views of the same spatial relation and fail to respond when that relation changes. Addressing these failures requires…
One Step, One Lead: Mitigating Higher-Order Interference in Multi-Domain Reinforcement Learning via Cross-Step Control
Zihan Lin, Xiaohan Wang, Jie Cao +4
Reinforcement learning (RL) across multiple domains can broaden the reasoning capabilities of large language models (LLMs), yet joint training often degrades individual-domain perf…
When Not to Imitate: Boundary-Aware Skill Memory for Reliable Tool-Use LLM Agents
Zihan Lin, Zhenyu Chen, Jiawen Wei +6
Extracting skills from past successes is critical for the efficient evolution of Large Language Model (LLM) agents. Prevailing agent self-evolution paradigms typically rely on a co…
ResRL: Boosting LLM Reasoning via Negative Sample Projection Residual Reinforcement Learning
Zihan Lin, Xiaohan Wang, Jie Cao +6
Reinforcement Learning with Verifiable Rewards (RLVR) enhances reasoning of Large Language Models (LLMs) but usually exhibits limited generation diversity due to the over-incentivi…
VitaLLM: A Versatile and Tiny Accelerator for Mixed-Precision LLM Inference on Edge Devices
Zi-Wei Lin, Tian-Sheuan Chang
We present VitaLLM, a mixed precision accelerator that enables ternary weight large language models to run efficiently on edge devices. The design combines two compute cores, a mul…
AWPO: Enhancing Tool-Use of Large Language Models through Adaptive Integration of Reasoning Rewards
Zihan Lin, Xiaohan Wang, Hexiong Yang +5
While Reinforcement Learning (RL) shows promise in training tool-use Large Language Models (LLMs) using verifiable outcome rewards, existing methods largely overlook the potential…
ResT: Reshaping Token-Level Policy Gradients for Tool-Use Large Language Models
Zihan Lin, Xiaohan Wang, Jie Cao +4
Large language models (LLMs) transcend passive generation and act as goal-directed agents by invoking external tools. Reinforcement learning (RL) offers a principled framework for…