---

name: training-evaluation description: "Guide safe use of the learning-to-learn training and evaluation CLIs without depending on the source checkout." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

training-evaluation

Use this sub-skill when you need to run, adapt, or troubleshoot the repo's train.py and evaluate.py workflows.

Use this for

• Tiny CPU smoke commands for training and evaluation.
• Saved-optimizer directory handling for L2L reloads.
• Choosing between optimizer=Adam and optimizer=L2L.
• Understanding log / evaluation cadence and unroll math.

Route elsewhere when

• You need meta-optimizer internals, variable interception, or save/load semantics beyond the CLI surface: ../meta-optimizer-api/SKILL.md
• You need problem construction, dataset mode selection, MNIST/CIFAR data caveats, or custom loss guidance: ../problem-factories/SKILL.md

Safe default

Prefer the simple scalar problem, one epoch, two steps, and a one-step unroll. For save/load smoke checks, point --save-path at a fresh directory and let the helper script choose a save-triggering evaluation period.

Read next

• references/cli-reference.md
• references/workflows.md
• references/troubleshooting.md
• scripts/l2l_cli_smoke.py