How can world models enter the embodied decision loop?
How can environmental prediction support long-horizon planning, causal reasoning, counterfactual imagination, and closed-loop control—not just generation?
EMBODIED INTELLIGENCE · OPEN-SOURCE CONTRIBUTOR
Building agents
that learn in the world.
I am a research intern at the Institute of Automation, Chinese Academy of Sciences, working on embodied intelligence and world models, robot learning and reinforcement learning, and next-generation agentic architectures for embodied systems.
On GitHub, I turn engineering questions in RL post-training, agent runtimes, and distributed systems into reproducible, testable experiments through open-source collaboration.
01 / UPSTREAM WORK
Turning research questions
into verifiable systems.
Selected public pull requests by YAO-001. Status and statistics last checked on 2026.08.30. Open a row for the summary and original PR.
01Recoverable tool errors in CodeModeFASTMCP · PR #4704Agent Runtime · MCPMERGED
Fixed FastMCP CodeMode so tool exceptions flow through guest try/except, preserving intermediate results while keeping uncaught ToolError semantics explicit.
02Per-turn generation tracing for Agentic RLVERL · PR #7204Agentic RL · ObservabilityMERGED
Added prompt_text and response_text to every LLM turn in asynchronous Agent Loops, allowing MLflow, Weave, and Trackio to reconstruct multi-turn trajectories without changing the public API.
03GPU numerical references for native AttentionARENO · PR #473GPU Kernels · VerificationMERGED
Built GPU equivalence tests for varlen prefill and paged decode attention against independent PyTorch SDPA math, covering gradients, GQA, causal and sliding-window masks, and KV-cache updates.
04Reliable launch environments for TMS OffloadAREAL · PR #1578Distributed RL · InfrastructureMERGED
Resolved the stdbuf and LD_PRELOAD collision in multi-GPU TMS offload, centralized local command construction, and validated the complete initialize → offload → destroy lifecycle.
05Race-safe teardown for FSDP / TMS trainingAREAL · PR #1571FSDP · Systems ReliabilityDRAFT
Designed re-entrant resume and cleanup, phased worker shutdown, monitored barriers, and TERM-to-KILL process-group escalation for teardown races after TMS offload, validated on two GPUs.
06Lower-memory GRPO and RLOO trainingHUGGING FACE TRL · PR #6615RL Post-training · MemoryDRAFT
Added opt-in activation offloading to GRPOTrainer and RLOOTrainer, extending memory optimization to reinforcement-learning post-training paths without changing default behavior.
07Selective weight decay under FSDPVERL · PR #7215FSDP · OptimizerOPEN
Fixed incorrect weight decay on bias and normalization parameters in FSDP, with standard parameter groups, legacy-checkpoint compatibility, and FSDP1/FSDP2 regression coverage.
02 / CURRENT QUESTIONS
The questions I am working through.
How can robots keep learning from data and interaction?
How can imitation learning, reinforcement learning, and online experience be combined so policies improve efficiently and robustly under real-world constraints?
What is the next abstraction for agentic embodied systems?
How should perception, memory, planning, tools, and action be coordinated so agents transfer across tasks while retaining interpretable boundaries in open worlds?
03 / ABOUT
I study embodied intelligence—and the systems that make the research reliable.
My research interests sit at the intersection of embodied intelligence, world models, robot learning, and reinforcement learning. I want to explore a more complete agentic embodied architecture—one that can perceive, remember, plan, use tools, act, and learn through continued interaction with the world.
Alongside research, I contribute to FastMCP, verl, AReaL, TRL, and AReno. Reliable training infrastructure, observable agent trajectories, and explicit tool boundaries are not secondary concerns to me; they are part of the research problem.
- Python
- PyTorch
- FSDP
- Reinforcement Learning
- Robot Learning
- Distributed Systems
- MCP