Posts by Jared Bowden

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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Optimizing LLM Workloads: AMD Instinct MI355X GPUs Drive Competitive Performance

AI training workloads are pushing the limits of modern GPU architectures. With the release of AMD ROCm™ 7.0 software, AMD is raising the bar for high-performance training by delivering optimized support for LLM workloads across the JAX and PyTorch frameworks. The latest v25.9 Training Dockers demonstrate exceptional scaling efficiency for both single-node and multi-node setups, empowering researchers and developers to push model sizes and complexity further than ever.

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