Longtao Zheng 郑龙韬
ltzheng01 at gmail dot com
Longtao Zheng is a Researcher at ByteDance, doing RL research for coding agents. His research interest is training open-ended agents in open-ended worlds. He completed his PhD at Nanyang Technological University (NTU) Singapore, advised by Prof. Bo An. Previously, he obtained his Bachelor's degree in computer science from University of Science and Technology of China (USTC) in 2022.
Dr. MAS: Stable Reinforcement Learning for Multi-Agent LLM Systems
Stable training algorithm and open-source codebase for multi-agent LLM RL
The Optimal Token Baseline: Variance Reduction for Long-Horizon LLM-RL
A token-level baseline prevents RL training collapse and reduces token consumption
Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations
Optimizing Triton kernel generation with multi-turn RL and test-time scaling
SimpleTIR: End-to-End Reinforcement Learning for Multi-Turn Tool-Integrated Reasoning
Simple trajectory filtering stabilizes multi-turn RL and emerges diverse reasoning
Towards Efficient Online Tuning of VLM Agents via Counterfactual Soft Reinforcement Learning
Fine-tuning VLM agents with online RL
Cradle: Empowering Foundation Agents Towards General Computer Control
An agent that can play AAA video games
MEMO: Memory-Guided Diffusion for Expressive Talking Video Generation
A SOTA and open-weight model for audio-driven talking video generation
AgentStudio: A Toolkit for Building General Virtual Agents
A trinity of environments, tools, and benchmarks for general virtual agents
A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist
The first multimodal agent for financial trading
Synapse: Trajectory-as-Exemplar Prompting with Memory for Computer Control
One of the earliest web agents with state abstraction, trajectory prompting, and memory
True Knowledge Comes from Practice: Aligning Large Language Models with Embodied Environments via Reinforcement Learning
Fine-tuning LLM agents with online RL
Controlling Type Confounding in Ad Hoc Teamwork with Instance-wise Teammate Feedback Rectification
A causality-based solution to deal with type confounding in ad hoc teamwork
Multi-Agent Multi-Game Entity Transformer: Towards Generalist Models in MARL
A generalist transformer for Honor of Kings, Starcraft II, and Neural MMO
Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning
Autocurricula for MARL in complex sparse-reward environments like Google Football