NVIDIA-NeMo / NVIDIA-NeMo/Switchyard
Add a reproducible NeMo Gym example for comparing routing strategies
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- Dominant language
- Rust
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Description
Motivation
Switchyard’s existing benchmark example shows how to evaluate routing with Harbor and coding-agent benchmarks. Switchyard also integrates with NeMo Gym, which supports a broader collection of benchmarks and agent harnesses, but the Switchyard repository does not currently include a runnable example of that workflow.
Adding one would help users:
- Evaluate routing strategies on benchmarks beyond Harbor’s coding-agent workloads.
- Compare a routed strategy with a fixed-model baseline using the same tasks.
- Reproduce the workflow directly from the Switchyard repository.
- Discover and validate the NeMo Gym integration without assembling commands from external documentation.
Proposed solution
Add benchmark/nemo_gym/ with a small, executable, version-pinned example that:
- Defines a fixed-model baseline and one routed strategy.
- Runs both against the same benchmark and task subset.
- Writes each condition to a separate output directory.
- Pairs rollouts before comparing rewards.
- Compares model tokens, classifier tokens, latency, and routing statistics.
- Explains the generated provenance and statistics artifacts.
- Links to the full NeMo Gym integration documentation.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by comparing the existing benchmark example with the proposed benchmark/nemo_gym/ directory and review the linked NeMo Gym integration documentation. The example should run both a fixed-model baseline and a routed strategy on the same task subset, produce separate outputs, pair rollouts for reward comparison, and explain the provenance and statistics artifacts.
Written by the indexing model from the issue text.
Assessment
- Domain
- testing-qa, tooling
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Active
- Clarity
- Mostly clear
- Newbie friendliness
- 55/100