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nvidia-nemo-gym-debugging【开发与工程】Use when debugging a Nemo Gym run or reward profiling job. Covers rollout collection failures, empty or partial JSONL outputs, stale materialized inputs, verifier/schema errors, Ray or Slurm issues, vLLM readiness, judge failures, tool/sandbox failures, cache problems, and throughput bottlenecks.
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Use this skill when something failed or looks suspicious in a Nemo Gym run. If the task is adding a new env, use the nemo-gym-env-integration skill; if it is changing profiling behavior, use the nemo-gym-reward-profiling skill.
Debug by classification, not by guessing. The first goal is to decide whether the issue is:
/models availability.resume_from_cache was enabled, stale materialized inputs are a first-class suspect.references/vllm-tool-call-schema-checks.md and run a static tool-schema check before changing Gym wrappers.++global_aiohttp_client_request_debug=True. Read references/request-boundary-visibility.md before changing code.references/error-profiles.md to classify the failing layer before changing code or data.references/diagnostic-snippets.md when you need copy-paste commands to inspect logs, output counts, materialized inputs, rollout JSONL shape, server readiness, or reward summaries without mutating run state.references/vllm-tool-call-schema-checks.md when a tool-call dataset may be rejected by vLLM/Outlines grammar compilation before any meaningful generation happens.references/request-boundary-visibility.md when /run 500s hide row identity or nested Gym 500s hide the inner model/verifier/provider error. It covers the existing Gym debug flag, shipped request-boundary markers, empty provider bodies, and vLLM provider-side escalation.When reporting back, state: