37 citations · 39 across the 12 of their papers we have counts for
15 papers
VenusRL: A Fully Disaggregated Agentic RL System with Priority Scheduling and Scalable Interaction
Mingjun Zhang, Yucheng Li, Menghao Zhang +15
Agentic Reinforcement Learning (RL) trains LLM agents through multi-turn interactions with external tool environments. Its multi-turn nature exposes two system-level bottlenecks un…
Semantic Projection for Continual Self-Evolution of Language Agents
Ziyu Liu, Jun Chen, Lixu Wang
Language-model agents increasingly rely on persistent natural-language skills to adapt beyond their frozen model parameters. When a shared skill is repeatedly revised from a non-st…
GTA-RAG: Graph-Trajectory-Augmented Reinforcement Learning for Multi-Turn Retrieval-Augmented Reasoning
Jun Chen, Yongchao Liu, Pengyu Qiu +6
Retrieval-augmented generation (RAG) enables LLMs to access external knowledge for answering knowledge-intensive questions. For complex multi-hop questions, multi-turn retrieval-au…
Kimi K3: Open Frontier Intelligence
Kimi Team, Tongtong Bai, Yifan Bai +398
We introduce Kimi K3, a 2.8T parameter Mixture-of-Experts model with 104 billion activated parameters, native vision capabilities, and a 1-million-token context window. Kimi K3 is…
Dual-Process Atomic Skill Learning: Decoupling Semantic Reasoning and Real-Time Control
Jun Chen, Erdent Bao, Erdemt Bao +8
Language-conditioned Imitation Learning (IL) is essential for enabling robots to perform complex tasks following natural language instructions. However, generalizing to multi-step…
NFTR: From Provable Mode-Averaging to Geodesic Subgoal Selection in Offline Goal-Conditioned RL
Erdemt Bao, Xing Lei, Jun Chen +2
Hierarchical Implicit Q-Learning (HIQL), an offline goal-conditioned RL method, selects subgoals by value-function advantages alone. This rule has two coupled failure modes. Optimi…
Oyster-II: Reinforcement Learning for Constructive Safety Alignment in Large Language Models
Jiyang Guan, Yong Xie, Jun Chen +6
Large language models (LLMs) have demonstrated remarkable capabilities across diverse applications, yet ensuring their simultaneous safety, helpfulness, and trustworthiness remains…
ANCORA: Learning to Question via Manifold-Anchored Self-Play for Verifiable Reasoning
Chengcao Yang
We propose a paradigm shift toward open-ended curriculum self-play: rather than learning to answer on a fixed prompt set, a unified policy learns to question: generating verifiable…
Enhancing Multimodal In-Context Learning via Inductive-Deductive Reasoning
Haoyu Wang, Haonan Wang, Yuyan Chen +5
In-context learning (ICL) allows large models to adapt to tasks using a few examples, yet its extension to vision-language models (VLMs) remains fragile. Our analysis reveals that…
Kimi K2.5: Visual Agentic Intelligence
Kimi Team, Tongtong Bai, Yifan Bai +333
We introduce Kimi K2.5, an open-source multimodal agentic model designed to advance general agentic intelligence. K2.5 emphasizes the joint optimization of text and vision so that…
VideoAuto-R1: Video Auto Reasoning via Thinking Once, Answering Twice
Shuming Liu, Mingchen Zhuge, Changsheng Zhao +20
Chain-of-thought (CoT) reasoning has emerged as a powerful tool for multimodal large language models on video understanding tasks. However, its necessity and advantages over direct…
Reinforcement Learning from User Feedback
Eric Han, Jun Chen, Karthik Abinav Sankararaman +8
As large language models (LLMs) are increasingly deployed in diverse user facing applications, aligning them with real user preferences becomes essential. Existing methods like Rei…
Fact-R1: Towards Explainable Video Misinformation Detection with Deep Reasoning
Fanrui Zhang, Dian Li, Qiang Zhang +6
The rapid spread of multimodal misinformation on social media has raised growing concerns, while research on video misinformation detection remains limited due to the lack of large…
LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding
Xiaoqian Shen, Yunyang Xiong, Changsheng Zhao +14
Multimodal Large Language Models (MLLMs) have shown promising progress in understanding and analyzing video content. However, processing long videos remains a significant challenge…
An Introduction to Vision-Language Modeling
Florian Bordes, Richard Yuanzhe Pang, Anurag Ajay +38
Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From having a visual assistant that could guid…