4 citations · 6 across the 20 of their papers we have counts for
6 papers · 1 filter
When Importance Sampling Misallocates Credit: Asymmetric Ratios for Outcome-Supervised RL
Jiakang Wang, Runze Liu, Qingpeng Cai +7
Reinforcement learning (RL) has shown great promise in large language models (LLMs) post-training, which typically rely on token-level clipping to maintain stability during optimiz…
RLEP: Reinforcement Learning with Experience Replay for LLM Reasoning
Hongzhi Zhang, Jia Fu, Jingyuan Zhang +4
Reinforcement learning (RL) for large language models is an energy-intensive endeavor: training can be unstable, and the policy may gradually drift away from its pretrained weights…
Stabilizing Knowledge, Promoting Reasoning: Dual-Token Constraints for RLVR
Jiakang Wang, Runze Liu, Fuzheng Zhang +4
Reinforcement Learning with Verifiable Rewards (RLVR) has become an effective post-training method for improving the reasoning abilities of Large Language Models (LLMs). However, e…
DynTok: Dynamic Compression of Visual Tokens for Efficient and Effective Video Understanding
Hongzhi Zhang, Jingyuan Zhang, Xingguang Ji +2
Typical video modeling methods, such as LLava, represent videos as sequences of visual tokens, which are then processed by the LLM backbone for effective video understanding. Howev…
Data Metabolism: An Efficient Data Design Schema For Vision Language Model
Jingyuan Zhang, Hongzhi Zhang, Zhou Haonan +7
Data curation plays a crucial role in training powerful Visual Language Models (VLMs). In this work, we introduce the concept of Data Metabolism and present our data-centric framew…
Capybara-OMNI: An Efficient Paradigm for Building Omni-Modal Language Models
Xingguang Ji, Jiakang Wang, Hongzhi Zhang +6
With the development of Multimodal Large Language Models (MLLMs), numerous outstanding accomplishments have emerged within the open-source community. Due to the complexity of creat…