6 papers
Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning
Zhenyu Hou, Yujiang Li, Jie Tang +1
Reinforcement learning (RL) is becoming increasingly important for post-training large language models (LLMs). Previous RL pipelines for LLMs were mostly synchronous and batch-inte…
GLM-5V-Turbo: Toward a Native Foundation Model for Multimodal Agents
V Team, Wenyi Hong, Xiaotao Gu +94
We present GLM-5V-Turbo, a step toward native foundation models for multimodal agents. As foundation models are increasingly deployed in real environments, agentic capability depen…
SceneGenAgent: Precise Industrial Scene Generation with Coding Agent
Xiao Xia, Dan Zhang, Zibo Liao +5
The modeling of industrial scenes is essential for simulations in industrial manufacturing. While large language models (LLMs) have shown significant progress in generating general…
T1: Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling
Zhenyu Hou, Xin Lv, Rui Lu +6
Large language models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks. However, existing approaches mainly rely on imitation learning and struggle to ac…
Controlling Large Language Model with Latent Actions
Chengxing Jia, Ziniu Li, Pengyuan Wang +4
Adapting Large Language Models (LLMs) to downstream tasks using Reinforcement Learning (RL) has proven to be an effective approach. However, LLMs do not inherently define the struc…
LongReward: Improving Long-context Large Language Models with AI Feedback
Jiajie Zhang, Zhongni Hou, Xin Lv +7
Though significant advancements have been achieved in developing long-context large language models (LLMs), the compromised quality of LLM-synthesized data for supervised fine-tuni…