2 citations · 5 across the 10 of their papers we have counts for
12 papers
GRPO-VPS: Enhancing Group Relative Policy Optimization with Verifiable Process Supervision for Effective Reasoning
Jingyi Wang, Lei Zhu, Tengjin Weng +8
Reinforcement Learning with Verifiable Rewards (RLVR) has advanced the reasoning capabilities of Large Language Models (LLMs) by leveraging direct outcome verification instead of l…
What Makes Low-Bit Quantization-Aware Training Work for Reasoning LLMs? A Systematic Study
Keyu Lv, Manyi Zhang, Xiaobo Xia +6
Reasoning models excel at complex tasks such as coding and mathematics, yet their inference is often slow and token-inefficient. To improve the inference efficiency, post-training…
E-Pruner: Towards Efficient, Economical, and Effective Layer Pruning for Large Language Models
Tao Yuan, Haoli Bai, Yinfei Pan +5
With the increasing size of large language models, layer pruning has gained increased attention as a hardware-friendly approach for model compression. However, existing layer pruni…
TurnGuide: Enhancing Meaningful Full Duplex Spoken Interactions via Dynamic Turn-Level Text-Speech Interleaving
Wenqian Cui, Lei Zhu, Xiaohui Li +4
Full-Duplex Speech Language Models (FD-SLMs) are specialized foundation models designed to enable natural, real-time spoken interactions by modeling complex conversational turn-tak…
Fourier Compressor: Frequency-Domain Visual Token Compression for Vision-Language Models
Huanyu Wang, Jushi Kai, Haoli Bai +4
Vision-Language Models (VLMs) incur substantial computational overhead and inference latency due to the large number of vision tokens introduced by high-resolution image and video…
A Simple Linear Patch Revives Layer-Pruned Large Language Models
Xinrui Chen, Haoli Bai, Tao Yuan +7
Layer pruning has emerged as a widely used technique for compressing large language models (LLMs). However, existing layer pruning approaches often incur substantial performance de…
Faster and Better LLMs via Latency-Aware Test-Time Scaling
Zili Wang, Tianyu Zhang, Haoli Bai +5
Test-Time Scaling (TTS) has proven effective in improving the performance of Large Language Models (LLMs) during inference. However, existing research has overlooked the efficiency…
Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models
Ruikang Liu, Yuxuan Sun, Manyi Zhang +5
Recent advancements in reasoning language models have demonstrated remarkable performance in complex tasks, but their extended chain-of-thought reasoning process increases inferenc…
FastAttention: Extend FlashAttention2 to NPUs and Low-resource GPUs
Haoran Lin, Xianzhi Yu, Kang Zhao +17
FlashAttention series has been widely applied in the inference of large language models (LLMs). However, FlashAttention series only supports the high-level GPU architectures, e.g.,…
FlatQuant: Flatness Matters for LLM Quantization
Yuxuan Sun, Ruikang Liu, Haoli Bai +10
Recently, quantization has been widely used for the compression and acceleration of large language models (LLMs). Due to the outliers in LLMs, it is crucial to flatten weights and…
MoPE-CLIP: Structured Pruning for Efficient Vision-Language Models with Module-wise Pruning Error Metric
Haokun Lin, Haoli Bai, Zhili Liu +5
Vision-language pre-trained models have achieved impressive performance on various downstream tasks. However, their large model sizes hinder their utilization on platforms with lim…
IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact
Ruikang Liu, Haoli Bai, Haokun Lin +6
Large language models (LLMs) excel in natural language processing but demand intensive computation. To mitigate this, various quantization methods have been explored, yet they comp…