Posts by Mingjie Lu

veRL on AMD: Production-Ready RL Post-Training on ROCm

Reinforcement learning post-training on AMD Instinct GPUs is here — with a turnkey container, AITER-accelerated vLLM and SGLang rollout, and accuracy validated on both MI300 and MI355.

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Scaling RL with verl on AMD Instinct MI355X: Async Walkthrough and Sync Benchmark

Reinforcement learning (RL) for large language models (LLMs) alternates between two phases: generation (rollout), where the current policy produces responses, and training, where those responses are used to update the policy. In verl, the key design choices are when these phases run relative to each other (synchronously or with overlap) and where they run (colocated on the same GPUs or on separate GPU pools). This blog first explains the differences between the two modes and when to use each.

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Accelerating Autonomous Driving Model Training on AMD ROCm™ Software

The autonomous driving industry is undergoing rapid growth, driven by advances in AI and the increasing demand for safer, smarter transportation. At the core of this transformation are complex deep learning models that enable vehicles to perceive, reason, and navigate dynamic environments. Efficiently training these models at scale has become a key challenge for companies working to bring autonomous systems to the road.

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