Cheng Qian

How Far Can Unsupervised RLVR Scale LLM Training?

Teaching Language Model to Act Efficiently

RM-R1: Reward Modeling as Reasoning

UserRL: Training Interactive User-Centric Agent via Reinforcement Learning

Geometri-Disentangelment Unlearning

LoCoBench: A Benchmark for Long-Context Large Language Models in Complex Software Engineering

Self-Improving LLM Agents at Test-Time

EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents

ToolRL: Reward is All Tool Learning Needs

AIR: A Systematic Analysis of Annotations, Instructions, and Response Pairs in Preference Dataset

Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance

Improving Zero-Shot Generalization of Instruction Tuning by Data Arrangement