I'm interested in building trainable and reliable AI agents, with a focus on LLM post-training, Agentic RL, agent infrastructure, and industrial AI systems.

• LLM Post-Training

  • SFT, Preference Optimization, RLHF / RLVR
  • PPO, GRPO and reinforcement learning for tool-using agents
  • Data synthesis, trajectory construction and bad-case mining

• Agentic RL & Agent Training

  • Multi-turn and long-horizon agent training
  • Tool-use and environment interaction
  • Trajectory collection and credit assignment
  • Verifiers, reward design and executable evaluation environments

• Agent Infrastructure

  • Agent Harness / Runtime
  • Tool execution, sandboxing and state management
  • Rollout systems and training-serving integration
  • Evals, observability and failure recovery

• Industrial AI

  • AI agents for CAD / CAE / engineering software
  • GUI + API hybrid agents
  • Verifiable workflows for engineering tasks
  • Turning industrial software into trainable agent environments

Languages Python · C++ · Go

LLM / Training PyTorch · Transformers · verl · DeepSpeed · Megatron-LM

Inference vLLM · SGLang

Systems CUDA · Docker · Kubernetes · Linux

Agentic RL · Post-Training · RL Infrastructure · Agent Harness
Long-Horizon Agents · Verifiable Rewards · Industrial Agents

🚀 My current goal is to understand and build the complete agent learning loop:

Environment → Rollout → Trajectory → Verifier → Reward → Post-Training → Evaluation → Deployment

and explore how this loop can be applied to real-world industrial software and engineering workflows.