Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/nvidia-nemo/megatron-bridge/nemo-rl-e2e-testingnpx skills add NVIDIA-NeMo/Megatron-Bridge --skill nemo-rl-e2e-testinggit clone --depth 1 https://github.com/NVIDIA-NeMo/Megatron-BridgeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nvidia-nemo/megatron-bridge/nemo-rl-e2e-testing)<a href="https://agentmods.dev/skills/nvidia-nemo/megatron-bridge/nemo-rl-e2e-testing"><img src="https://agentmods.dev/badge/skills/nvidia-nemo/megatron-bridge/nemo-rl-e2e-testing.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00154 | $0.07141 |
| Opus 5 | $0.00077 | $0.03571 |
| Sonnet 5 | $0.00031 | $0.01428 |
| Haiku 4.5 | $0.00015 | $0.00714 |
Grade A, and why
nemo-rl-e2e-testing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 557 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NeMo-RL E2E Testing
Validate a Megatron-Bridge model or training API change through NeMo-RL's Megatron backend. This catches integration issues that Bridge-only tests miss: NeMo-RL-owned rollout scheduling, reward handling, policy/reference setup, HF import/export through Bridge, optimizer setup, checkpoint ownership, and policy-to-generation weight transfer.
Use this as an external compatibility smoke test after the focused Bridge tests for the model/provider change pass.
This is not a replacement for Bridge model parity tests. A NeMo-RL GRPO or SFT run proves that Bridge can survive an external RL training loop; architecture correctness still comes from Bridge import/export, logits, roundtrip, and model-specific inference tests.
Scope
Think in coverage levels. Start with Level 0 and add only the levels justified by the change.
| Level | Required when | What it proves |
|---|---|---|
| 0: Megatron policy GRPO smoke | Any new provider or provider config change that claims NeMo-RL compatibility | NeMo-RL can import the local Bridge provider, build a Megatron policy, initialize optimizer/scheduler state, run rollout/ref/logprob wiring, and finish a short GRPO job |
| 1: LoRA/checkpoint variant | Checkpointing, HF export, optimizer state, resume behavior, or a NeMo-RL-supported PEFT path changed | NeMo-RL can save through its checkpoint schedule, resume without losing training state, and, when PEFT is enabled in that NeMo-RL checkout, apply Bridge LoRA hooks |
| 2: Non-colocated vLLM refit | HF export, weight mapping, policy-to-generation refit, delta compression, packed transfer, or vLLM update behavior changed | Bridge-exported weights can be transferred from the Megatron policy worker into separate vLLM generation workers |
| 3: Optional Megatron generation backend | Only when the NeMo-RL checkout still supports policy.generation.backend=megatron and the change explicitly targets that path |
NeMo-RL can use Megatron for both policy and generation rather than only vLLM generation |
| 4: Parallelism stress | TP/PP/CP/EP, sequence parallel, MoE dispatch, pipeline stage layout, or distributed optimizer behavior changed | Provider settings remain correct under non-trivial Megatron parallel state |
| 5: Architecture-specific e2e | VLM, audio, MoE, MTP/draft models, FP8/QAT/ModelOpt, quantized weights, or custom layers are involved | The architecture-specific runtime path is exercised, not just a text-only dense GRPO smoke |
| 6: Learning signal | Optimizer, scheduler, loss, reward, PEFT trainability, gradient flow, or training stability changed | Metrics move in the expected direction over a short run and do not silently produce zero/NaN/unstable updates |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 557 lines · 154 tokens per session scan A d11394529d73
nemo-rl-e2e-testing is a skill published in the GitHub repository NVIDIA-NeMo/Megatron-Bridge (900 stars, last pushed yesterday), licensed Apache-2.0. It adds 154 tokens to every session and 7,141 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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