Nemo Rl Auto Research

NVIDIAOfficialPublished by NVIDIA

Skill
Workflow
Software development
Apache-2.0
Medium risk

From the author

Autonomous NeMo-RL research agent workflow for directed hypothesis testing and open-ended discovery. Guides agents through the full experiment lifecycle: understanding recipes and environments, wiring RL or NeMo-gym runs, launching reproducible baselines and iterations, analyzing results, preserving human oversight, and using git plus TSV logs as the research ledger. Do NOT use for: bug fixes, code review, documentation, refactoring, dependency updates, or single-file changes.

— the description by NVIDIA

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The nemo-rl-auto-research skill provides a structured, autonomous workflow for conducting hypothesis-driven research in NeMo-RL and NeMo-gym environments. It manages the full experiment lifecycle, including environment inspection, baseline establishment, iterative testing, and result logging. The skill enforces reproducibility by using git branches for every experiment and maintaining a centralized TSV ledger for tracking metrics, resource usage, and campaign progress. It is designed for directed discovery and hypothesis testing, requiring human oversight for planning and approval of compute-intensive tasks.

What it does

Automates the execution of iterative RL research experiments.
Maintains a git-based research ledger with branch-per-experiment tracking.
Logs experiment metrics, resource usage, and status in a structured TSV format.
Enforces user-defined stop conditions like experiment counts or time budgets.
Supports various execution environments including local, Slurm, and Kubernetes.
Provides progress updates and result summaries throughout the campaign.

When to use it

When running long-term research campaigns for NeMo-RL or NeMo-gym.
When testing specific hypotheses regarding model accuracy, reward, or throughput.
When you need to systematically explore recipe variations while maintaining reproducibility.
When you require a structured way to track and log multiple experimental iterations.

Advantages

Ensures reproducibility through strict git branching and logging practices.
Reduces manual overhead in managing experiment lifecycles and metric collection.
Provides clear visibility into campaign progress and stop conditions.
Prevents accidental loss of work by isolating experiments into separate branches.
Enables data-driven decision making by centralizing results in a searchable ledger.

Usage examples

“Run 20 experiments to improve the validation accuracy of the current dapo-qwen2.5 recipe.”: The agent will initialize a campaign, create a baseline, and iteratively test 20 variations, logging results to the TSV and stopping once the count is reached.
“Conduct a 5-hour research campaign testing different rollout batch sizes for the current model.”: The agent will calculate a deadline, execute experiments within the time limit, and provide status updates on remaining time and progress.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/nemo-rl-auto-research/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip nemo-rl-auto-research.zip -d .agents/skills

Documentation by NVIDIA

Auto Research

Run iterative NeMo-RL experiments in this repository against the user's stated objective, such as accuracy, reward, throughput, latency, stability, or another recipe-specific metric, with git as the research ledger.

Treat dependencies as ready, but choose the runtime deliberately. Use the recipe's authoritative metric as the source of truth. Keep changes small, reproducible, and simple. Preserve unrelated user work.

Safety: This skill creates git branches, writes files to disk, and executes shell commands including training jobs that may consume GPU resources. Always confirm the campaign plan with the user before creating branches or launching jobs. Do not execute destructive git operations (reset, force-push) or launch compute-intensive jobs without explicit user approval.

Use the nemo-rl-session-memory skill for every auto-research campaign. Start or resume a session record before branching, then checkpoint after forming the plan, before and after meaningful edits or long-running launches, when the user changes direction, and before handoff or final summary.

After context compaction, handoff, disconnect, or a long gap, reload this skill and any companion skills already in use, read the latest nemo-rl-session-memory handoff, and restate the overall objective, stop rules, current branch, and latest result before continuing. Treat follow-up steering as additive unless the user explicitly changes the main objective.

Workflow

Inspect the current git state and identify unrelated user changes before branching.
Use a shared branch prefix. Prefer a user-provided one; otherwise create a suggestive default such as autoresearch/2026-03-24-dapo-qwen2p5.
Read the target recipe, its parents, and the relevant code paths in examples/run_grpo.py, nemo_rl/models/, nemo_rl/algorithms/, nemo_rl/environments/, and docs/. For NeMo-gym recipes, also inspect examples/nemo_gym/ entrypoints, configs, and launch scripts.
Translate any user stop rule into explicit values you can monitor, such as the requested number of experiments as target_experiment_count, campaign_deadline, per_experiment_timeout, or target_metric.
Verify required data, checkpoints, runtime inputs, and the launcher.
Create an untracked TSV log and per-experiment log directory.
Run a baseline first on <prefix>/baseline if none exists.

For GPU, CPU-heavy, distributed, or long-running work, choose the execution environment deliberately. Run locally when the current machine has suitable GPUs and capacity; otherwise follow the user's requested environment, use launch-nemo-rl for nrl-k8s/Kubernetes, use the environment's native launcher for Slurm, or clarify with the user before launching. Use CPU-only local runs only for light inspection, dry runs, and short non-GPU checks.

If the user mentions Brev, or if /home/ubuntu/RL exists and /ephemeral is available as a volume, treat the machine as a Brev instance and use nemo-rl-brev-etiquette before creating experiment directories, caches, logs, checkpoints, or authenticated runtime state.

Branching

Put every experiment on its own branch under the shared prefix.
Keep every branch, even for failed or weak ideas.
Put at least one commit on each branch for the hypothesis.
Add follow-up fix commits on the same branch when a rerun is justified.
Never stash, reset, or overwrite unrelated user changes silently. If dirty files overlap the experiment, use a separate worktree or ask before proceeding.

See references/git-workflow.md for the exact pattern.

Loop

Pick one concrete hypothesis.
Create a branch such as autoresearch/2026-03-24-dapo-qwen2p5/prompt-compact-schema.
Edit the smallest set of files needed.
Commit the hypothesis.
Before launching the run, check the monitored stop conditions. Do not stop early unless one is already clearly met.
Identify the authoritative metric source from the recipe or logging code, then run with a unique log path:

LOG_DIR=reports/auto_research/<campaign>/<experiment>
mkdir -p "$LOG_DIR"
uv run <entrypoint> > "$LOG_DIR/run.log" 2>&1

If the user gave a per-experiment wall-clock limit, enforce it explicitly. Prefer a recipe-level timeout when one already exists; otherwise wrap the command with an external timeout. If both exist, honor the tighter limit.
Extract the primary metric with a command appropriate for the actual log format. If extraction is empty, inspect the last log lines and the recipe's logging path before marking the run.
Record index, branch, parent commit, commit, recipe, metric name, metric value, memory (GB), elapsed time (minutes), launcher, job id, command, log path, status, and description in the TSV, along with enough timing or count information to evaluate the stop rule.
Periodically print user-facing progress updates during the campaign. Include the current branch, latest known result, attempted experiment count, remaining experiment count if applicable, remaining campaign time if applicable, and whether any stop condition has been met yet.
Re-check the monitored stop conditions after the experiment completes and state the result explicitly, for example stop condition not yet met: 17/24 attempted, 6h12m remaining or stop condition met: 24/24 attempted.
Mark the result as keep, discard, or crash, then move to the next branch unless a user-specified stop condition has been clearly met.

For count-based stop rules, count attempted ideas, not only successful or fully completed runs.

For campaign time budgets, convert the user limit into an absolute deadline at the start of the campaign and keep checking remaining time.

For per-experiment budgets, enforce a timeout on every run and treat overruns as failures.

Examples:

do 50 experiments: stop only after 50 attempted experiment rows exist in the TSV
10h total, 1h each: enforce a 1 hour limit per run and stop when the 10 hour campaign budget is reached, or when there is not enough remaining budget to start another 1 hour run
50 experiments or 10h total, 1h each: monitor all three values, never exceed the per-run cap, and stop only when one campaign-level stop trigger is clearly reached

Priorities

Prefer ideas with high expected objective gain and low complexity cost:

correctness and backend compatibility
prompt and rollout formatting
batch, sequence, and precision layout
optimizer and scheduler tuning
reward shaping, clipping, or scaling
dataset mix or validation changes
synchronous versus asynchronous execution based on hardware

All else equal, prefer simpler wins and avoid brittle hardware-specific hacks.

Avoid

Do not conclude a training idea failed from an underpowered smoke run. If a run uses tiny batch sizes, very few optimizer steps, or otherwise non-representative settings, treat it as plumbing validation only; scale to a meaningful batch size and train long enough to test the hypothesis before marking it discard.
Do not repeatedly pay batch-scheduler setup costs for tight edit-run-debug loops. If Slurm batch jobs have a large startup tax and failures require quick iteration, use the documented interactive Slurm pattern or ask the user before resubmitting more batch jobs.
Do not let context compaction or follow-up steering questions erase the original campaign goal. Refresh nemo-rl-session-memory, reload active skills, and preserve the main objective unless the user explicitly changes it.

Stop

If the user gives explicit stopping conditions, they override the generic rule. Do not stop because the search feels sufficient; stop only when the requested count, deadline, budget, or target condition has been clearly met.

During the campaign, explicitly inform the user whether the stop condition has been met. If not, report the remaining count, remaining time, or other remaining threshold in concrete terms.

If the user does not give explicit stopping conditions, run the baseline plus up to three low-risk experiments, then summarize the best result and ask before continuing.

References

references/git-workflow.md for branch, dirty-worktree, parent-commit, and baseline rules.
references/exploration-ideas.md for turning symptoms into concrete hypotheses.
references/experiment-log-template.md for the TSV schema and reproducibility fields.

Files

ATTRIBUTION.mdAdded by AutoGineer841 B
BENCHMARK.md4.2 KiB
LICENSELicence file added by AutoGineer11.2 KiB
SKILL.md9.0 KiB
evals/evals.json3.1 KiB
references/experiment-log-template.md2.5 KiB
references/exploration-ideas.md5.5 KiB
references/git-workflow.md3.8 KiB
skill-card.md4.0 KiB
skill.oms.sig5.2 KiB

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

Security

What our scanner noticed· Medium risk

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Attribution and licence

Published by

NVIDIA

Publisher

NVIDIA (Official)

Source

NVIDIA/skills · skills/nemo-rl-auto-research · commit fce3cca

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Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE-APACHE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-09

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