Training a Search-R1 agent with Flame: warm GPU services, gang scheduling, and incremental weight sync

I work on Flame, an open-source distributed engine for elastic workloads. We added a Search-R1-style training example that teaches Qwen2.5-3B-Instruct to search a BM25 corpus while answering HotpotQA questions. The training loop runs as Python services: vLLM executors generate trajectories, PyTorch trainer replicas update the policy using group-relative advantages, and Flame’s object cache carries batches and weight updates. The interesting infrastructure pieces: Warm executors: models, retr...