Stack Doctor

Stack Doctor is an RL environment (OpenEnv) that trains LLMs to diagnose GPU inference stack failures. 73 scenarios across vLLM, SGLang, FlashInfer, and TensorRT-LLM on NVIDIA H100/B200/SM121 and AMD MI300X/MI355X. The agent gets an incident ticket, logs, and opinions from 4 specialist sub-agents — at least one deliberately lies per incident. The model must investigate (inspect logs, query specialists, apply fixes) and submit a justified diagnosis. Six reward functions shape behavior: valid JSON, environment interaction, investigation quality (penalizes blind guessing), partial credit for correct failure family, justification quality, and efficiency. Trained Qwen2.5-1.5B with GRPO via Unsloth + TRL on H100. The 9B base scored +19.5 (near-oracle) with zero training, proving environment quality. The 1.5B starts at -4.9 and learns to investigate before diagnosing.