1 citations · 1 across the 13 of their papers we have counts for
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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…