Minimize idle accelerators: Native RL job interleaving with co-operative time-slicing in llm-d

The math behind reinforcement learning (RL) post-training for large language models (LLMs) is notoriously unforgiving. As frontier AI labs push the boundaries of reasoning and coding models using RL post-training algorithms like Group Relative Policy Optimization (GRPO), they routinely hit hard arch

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