1 citations · 1 across the 3 of their papers we have counts for
6 papers
MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe
Tianyu Yu, Zefan Wang, Chongyi Wang +31
Multimodal Large Language Models (MLLMs) are undergoing rapid progress and represent the frontier of AI development. However, their training and inference efficiency have emerged a…
AutoTriton: Automatic Triton Programming with Reinforcement Learning in LLMs
Shangzhan Li, Zefan Wang, Ye He +8
Kernel development in deep learning requires optimizing computational units across hardware while balancing memory management, parallelism, and hardware-specific optimizations thro…
RLPR: Extrapolating RLVR to General Domains without Verifiers
Tianyu Yu, Bo Ji, Shouli Wang +9
Reinforcement Learning with Verifiable Rewards (RLVR) demonstrates promising potential in advancing the reasoning capabilities of LLMs. However, its success remains largely confine…
GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K Resolution
Fengxiang Wang, Mingshuo Chen, Yueying Li +11
Ultra-high-resolution (UHR) remote sensing (RS) imagery offers valuable data for Earth observation but pose challenges for existing multimodal foundation models due to two key bott…
TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators
Jianling Li, Shangzhan Li, Zhenye Gao +9
Triton, a high-level Python-like language designed for building efficient GPU kernels, is widely adopted in deep learning frameworks due to its portability, flexibility, and access…
Process Reinforcement through Implicit Rewards
Ganqu Cui, Lifan Yuan, Zefan Wang +22
Dense process rewards have proven a more effective alternative to the sparse outcome-level rewards in the inference-time scaling of large language models (LLMs), particularly in ta…