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Kube SRE Gym is a self-improving RL environment where a small language model (Qwen3-1.7B) learns to diagnose and fix real Kubernetes production incidents from scratch. The agent interacts with a live GKE cluster via kubectl commands — OOMKills, CrashLoopBackOffs, and ImagePullBackOffs are real Kubernetes events, not simulations. An adversarial designer (Claude) creates targeted incidents based on the agent's tracked weaknesses, while a curriculum controller escalates difficulty as mastery improves. Training uses GRPO (TRL 0.29.0 + vLLM) with an LLM judge that scores SRE workflow quality using three expert personas (Junior/Senior/Principal). Within 8 episodes, the agent learned to discover cluster topology, identify fault types from pod status, and apply correct fixes — all from reward signal alone, with zero hardcoded knowledge of the cluster.

Team

Placement

1st Place