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Nemo Gym Debugging (Nous Research)
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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Installer dans ton projet
$ curl -fsSL https://arboristhree.xyz/cli/install-skill.mjs -o /tmp/arboris-install-skill.mjs && node /tmp/arboris-install-skill.mjs nemo-gym-debugging__nousresearch-gym__agents-skills-3d2e400f55f1Nous Research
NousResearch/gym
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--- name: nemo-gym-debugging description: >- 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. metadata: origin: Nous Research --- # Nemo Gym Debugging ## Invocation Check 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: - infra: Slurm, Ray, container, filesystem, network, ports - model serving: vLLM startup/readiness/throughput - config: wrong config bundle, missing agent, wrong extra args - data/schema: JSONL fields do not match verifier/resource server expectations - verifier/runtime: resource server exception or malformed verify response - cache/resume: stale materialized inputs or partial rollout output - throughput/resources: concurrency too high, judge bottleneck, tool/sandbox latency ## Debug Order 1. Check Slurm/Ray job state and logs. 2. Check vLLM readiness and `/models` availability. 3. Check Gym server readiness: all expected servers started. 4. Check tool routing if the env uses tools; check sandbox readiness only if a sandbox is configured. 5. Check materialized inputs and source data timestamps. 6. Check rollout output and profiling/metrics output counts. 7. Inspect the first real verifier exception, not shutdown noise. 8. Compare failing row schema against the resource server request model. ## High-Value Suspects - If data changed and `resume_from_cache` was enabled, stale materialized inputs are a first-class suspect. - If rollout output has a few rows and profiling is empty, inspect verifier errors and partial-output cache. - If all servers are ready but verifier returns 422/500, inspect request body schema before debugging infra. - If tool envs hang or partially work, check tool ownership/loading before changing model settings; check sandbox readiness only when a sandbox is actually part of the env. - If tool-call rows fail before generation with vLLM grammar/schema errors, read `references/vllm-tool-call-schema-checks.md` and run a static tool-schema check before changing Gym wrappers. - If logs only show nested "inner server" 500s without the real provider/verifier body, first enable existing request-boundary visibility with `++global_aiohttp_client_request_debug=True`. Read `references/request-boundary-visibility.md` before changing code. ## Reference Loading - Read `references/error-profiles.md` to classify the failing layer before changing code or data. - Read `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. - Read `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. - Read `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. ## Communication Pattern When reporting back, state: - observed symptom - failing layer - evidence from logs/files - likely cause - next concrete action
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Ce que fait Nemo Gym Debugging (Nous Research)
infra: Slurm, Ray, container, filesystem, network, ports
model serving: vLLM startup/readiness/throughput
config: wrong config bundle, missing agent, wrong extra args
data/schema: JSONL fields do not match verifier/resource server expectations
Comment utiliser Nemo Gym Debugging (Nous Research)
1
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2
L'agent installe le skill
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3
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