1 citations · 1 across the 13 of their papers we have counts for
13 papers
LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation
Tankun Li, Zhi Chen, Yaohua Tang
Post-training large language models (LLMs) via reinforcement learning (RL) has significantly advanced code generation capabilities. To bypass the heavy memory footprint of critic n…
TCPO: Turn-Level Credit Policy Optimization
Sicong Liao, Zhi Chen, Yaohua Tang
Verifier-guided reinforcement learning has become a powerful paradigm for improving LLM reasoning. In multi-turn settings, models receive a verifier score after each turn and itera…
Beyond the Best Teacher: Expanding and Compressing the Reasoning Solution Manifold
Songshuo Lu, Zhi Chen, Yaohua Tang
A single reinforcement-learning run can produce a strong reasoner yet an incomplete teacher: it often amplifies only a subset of the valid solution modes. We argue that reinforceme…
MusaCoder: Native GPU Kernel Generation with Full-Stack Training on Moore Threads GPU
Kun Cheng, Songshuo Lu, Sicong Liao +7
Native GPU kernel generation turns high-level tensor programs into executable, efficient low-level code. Existing Large Language Models (LLMs) struggle with this task, while execut…
ReorgGS: Equivalent Distribution Reorganization for 3D Gaussian Splatting
Luchao Wang, Kaimin Liao, Qian Ren +3
A converged 3D Gaussian Splatting (3DGS) model may approximate the target scene while remaining poorly parameterized for further optimization. We identify this failure mode as \emp…
LiteGUI: Lightweight GUI Agents via Multi-Solution Guided Distillation and Dual-Level Reinforcement Learning
Yubin Wu, Zicheng Cai, Liping Ning +4
We present LiteGUI, a new framework for building lightweight GUI agents. GUI interaction poses unique challenges due to the long-horizon nature of complex tasks and the existence o…
UniSem: Generalizable Semantic 3D Reconstruction from Sparse Unposed Images
Guibiao Liao, Qian Ren, Kaimin Liao +4
Semantic-aware 3D reconstruction from sparse, unposed images remains challenging for feed-forward 3D Gaussian Splatting (3DGS). Existing methods often predict an over-complete set…
URPO: A Unified Reward & Policy Optimization Framework for Large Language Models
Songshuo Lu, Hua Wang, Zhi Chen +1
Large-scale alignment pipelines typically pair a policy model with a separately trained reward model whose parameters remain frozen during reinforcement learning (RL). This separat…
DeskVision: Large Scale Desktop Region Captioning for Advanced GUI Agents
Yibin Xu, Liang Yang, Hao Chen +3
The limitation of graphical user interface (GUI) data has been a significant barrier to the development of GUI agents today, especially for the desktop / computer use scenarios. To…
StableGS: A Floater-Free Framework for 3D Gaussian Splatting
Luchao Wang, Qian Ren, Kaimin Liao +3
3D Gaussian Splatting (3DGS) reconstructions are plagued by stubborn ``floater" artifacts that degrade their geometric and visual fidelity. We are the first to reveal the root caus…
SEKI: Self-Evolution and Knowledge Inspiration based Neural Architecture Search via Large Language Models
Zicheng Cai, Yaohua Tang, Yutao Lai +3
We introduce SEKI, a novel large language model (LLM)-based neural architecture search (NAS) method. Inspired by the chain-of-thought (CoT) paradigm in modern LLMs, SEKI operates i…
Round Attention: A Novel Round-Level Attention Mechanism to Accelerate LLM Inference
Yaohua Tang, Zhicheng Hu, Kun Cheng +4
The increasing context window size in large language models (LLMs) has improved their ability to handle complex, long-text tasks. However, as the conversation rounds continue, it i…
TurboRAG: Accelerating Retrieval-Augmented Generation with Precomputed KV Caches for Chunked Text
Songshuo Lu, Hua Wang, Yutian Rong +2
Current Retrieval-Augmented Generation (RAG) systems concatenate and process numerous retrieved document chunks for prefill which requires a large volume of computation, therefore…