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nemo-gym-debugging

NVIDIA-NeMo/Gym/.agents/skills/nemo-gym-debugging/SKILL.md

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.

Skill1.2k starsChanged yesterday

What's in it

  1. Nemo Gym Debugging
  2. Invocation Check
  3. Debug Order
  4. High-Value Suspects
  5. Reference Loading
  6. Communication Pattern
---
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.
---

# 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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