# Noclue

- **Event:** [OpenEnv Hackathon SF](https://cerebralvalley.ai/e/openenv-hackathon-sf)
- **When:** Mar 7 at 9:00 AM – Mar 8 at 12:00 AM (PST)
- **Where:** Shack15, San Francisco, CA
- **Placement:** 1st Place
- **Team:** [Sidhartha Reddy Potu](https://cerebralvalley.ai/u/sid-rp), [Ashish Ranjan](https://cerebralvalley.ai/u/ashishranjan2404), [Guangting Yu](https://cerebralvalley.ai/u/GuangtingYu)
- **GitHub:** https://github.com/sid-rp/kube-sre-gym
- **Website:** https://huggingface.co/spaces/openenv-community/kube-sre-gym/blob/main/kube_sre_gym_colab.ipynb
- **Demo video:** https://youtu.be/3BWkMrsEFSc
- **Hugging Face:** https://huggingface.co/spaces/openenv-community/kube-sre-gym
- **Gallery:** https://cerebralvalley.ai/e/openenv-hackathon-sf/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/openenv-hackathon-sf/hackathon/gallery/51

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.

---

Markdown version of https://cerebralvalley.ai/e/openenv-hackathon-sf/hackathon/gallery/51. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt