22 citations · 51 across the 10 of their papers we have counts for
10 papers
IndexMem: Learned KV-Cache Eviction with Latent Memory for Long-Context LLM Inference
Xintong Yang, Hao Gu, Binxing Xu +6
Large Language Models (LLMs) are increasingly expected to operate over long contexts, yet standard softmax attention incurs a KV cache that grows linearly with sequence length, qui…
Bit-by-Bit: Progressive QAT Strategy with Outlier Channel Splitting for Stable Low-Bit LLMs
Binxing Xu, Hao Gu, Lujun Li +8
Training LLMs at ultra-low precision remains a formidable challenge. Direct low-bit QAT often suffers from convergence instability and substantial training costs, exacerbated by qu…
QaRL: Rollout-Aligned Quantization-Aware RL for Fast and Stable Training under Training--Inference Mismatch
Hao Gu, Hao Wang, Jiacheng Liu +9
Large language model (LLM) reinforcement learning (RL) pipelines are often bottlenecked by rollout generation, making end-to-end training slow. Recent work mitigates this by runnin…
Differentiable Skill Optimisation for Powder Manipulation in Laboratory Automation
Minglun Wei, Xintong Yang, Yu-Kun Lai +1
Robotic automation is accelerating scientific discovery by reducing manual effort in laboratory workflows. However, precise manipulation of powders remains challenging, particularl…
DDBot: Differentiable Physics-based Digging Robot for Unknown Granular Materials
Xintong Yang, Minglun Wei, Yu-Kun Lai +1
Automating the manipulation of granular materials poses significant challenges due to complex contact dynamics, unpredictable material properties, and intricate system states. Exis…
Differentiable Physics-based System Identification for Robotic Manipulation of Elastoplastic Materials
Xintong Yang, Ze Ji, Yu-Kun Lai
Robotic manipulation of volumetric elastoplastic deformable materials, from foods such as dough to construction materials like clay, is in its infancy, largely due to the difficult…
A Physics-informed Demonstration-guided Learning Framework for Granular Material Manipulation
Minglun Wei, Xintong Yang, Yu-Kun Lai +2
Due to the complex physical properties of granular materials, research on robot learning for manipulating such materials predominantly either disregards the consideration of their…
Recent Advances of Deep Robotic Affordance Learning: A Reinforcement Learning Perspective
Xintong Yang, Ze Ji, Jing Wu +1
As a popular concept proposed in the field of psychology, affordance has been regarded as one of the important abilities that enable humans to understand and interact with the envi…
Abstract Demonstrations and Adaptive Exploration for Efficient and Stable Multi-step Sparse Reward Reinforcement Learning
Xintong Yang, Ze Ji, Jing Wu +1
Although Deep Reinforcement Learning (DRL) has been popular in many disciplines including robotics, state-of-the-art DRL algorithms still struggle to learn long-horizon, multi-step…
An Open-Source Multi-Goal Reinforcement Learning Environment for Robotic Manipulation with Pybullet
Xintong Yang, Ze Ji, Jing Wu +1
This work re-implements the OpenAI Gym multi-goal robotic manipulation environment, originally based on the commercial Mujoco engine, onto the open-source Pybullet engine. By compa…