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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.

ou envoie-le directement à ton agent.

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-3d2e400f55f1
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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
Colle ce Markdown dans ton agent ou utilise les boutons ci-dessus pour l’écrire dans ton projet.

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
Copie le prompt
Un clic copie le prompt packagé (ou l'envoie à ton agent).
2
L'agent installe le skill
Il ajoute le SKILL.md et ses ressources à ton projet.
3
Activation automatique
Le skill s'active dès que le contexte correspond.

Déclencheurs pour Nemo Gym Debugging (Nous Research)

Dis simplement à ton agent quelque chose comme :

Applique le skill nemo-gym-debugging__nousresearch-gym__agents-skills-3d2e400f55f1 à cette tâche
Utilise nemo-gym-debugging__nousresearch-gym__agents-skills-3d2e400f55f1 pour améliorer cette implémentation
Passe en revue ce sujet avec nemo-gym-debugging__nousresearch-gym__agents-skills-3d2e400f55f1

Skills liés à Nemo Gym Debugging (Nous Research)