34 citations · 45 across the 32 of their papers we have counts for
8 papers · 1 filter
Kwai Summary Attention V2 Technical Report
OneRec Team, Chenglong Chu, Guohong Mu +30
Summary-token architectures such as Kwai Summary Attention (KSA) compress distant context into learnable Summary token per chunk, and hybrid variants interleave these layers with f…
Calibration-Aware Policy Optimization for Reasoning LLMs
Ziqi Wang, Xingzhou Lou, Meiqi Wu +2
Group Relative Policy Optimization (GRPO) enhances LLM reasoning but often induces overconfidence, where incorrect responses yield lower perplexity than correct ones, degrading rel…
EgoEverything: A Benchmark for Human Behavior Inspired Long Context Egocentric Video Understanding in AR Environment
Qiance Tang, Ziqi Wang, Jieyu Lin +3
Long context egocentric video understanding has recently attracted significant research attention, with augmented reality (AR) highlighted as one of its most important application…
QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Language-Action Models
Jingxuan Zhang, Yunta Hsieh, Zhongwei Wan +5
Vision-language-action (VLA) models unify perception, language, and control for embodied agents but face significant challenges in practical deployment due to rapidly increasing co…
Learning Intractable Multimodal Policies with Reparameterization and Diversity Regularization
Ziqi Wang, Jiashun Liu, Ling Pan
Traditional continuous deep reinforcement learning (RL) algorithms employ deterministic or unimodal Gaussian actors, which cannot express complex multimodal decision distributions.…
Position: The Hidden Costs and Measurement Gaps of Reinforcement Learning with Verifiable Rewards
Fang Wu, Aaron Tu, Weihao Xuan +21
Reinforcement learning with verifiable rewards (RLVR) is a practical, scalable way to improve large language models on math, code, and other structured tasks. However, we argue tha…
Step-3 is Large yet Affordable: Model-system Co-design for Cost-effective Decoding
StepFun, :, Bin Wang +195
Large language models (LLMs) face low hardware efficiency during decoding, especially for long-context reasoning tasks. This paper introduces Step-3, a 321B-parameter VLM with hard…
Model Extrapolation Expedites Alignment
Chujie Zheng, Ziqi Wang, Heng Ji +2
Given the high computational cost of preference alignment training of large language models (LLMs), exploring efficient methods to reduce the training overhead remains an important…