Two runnable examples, both CPU-only and network-free. Each one is executed as part of the release check, so if it is here it works.

pip install "miniverl[train]"      # CPU torch is enough

python examples/custom_environment/reverse_environment.py runs/examples
python examples/custom_teacher/sharpened_teacher.py       runs/examples

A complete new ToolEnvironment: two tools, three difficulties, a deterministic oracle, an exact verifier and a privileged context string for the teacher. It registers itself with @register, verifies that its own oracle solves 8/8 hard tasks, and then trains a toy student on it with genuine on-policy distillation.

The important line in its output is the oracle check:

registered environments: ['calculator', 'jsonnav', 'reverse', 'sqlite']
oracle solves 8/8 hard tasks

The final task-success number will usually be low or zero. That is expected: the toy models are around 200k parameters and the budget is a smoke test. The example demonstrates the extension point, not a capability. See docs/limitations.md.

Copy this file as the starting point for your own environment. The checklist for a correct one is in the "New environment" issue template.

A custom TeacherScorer that wraps the built-in local scorer and sharpens its top-k distribution before handing it to the loss. It shows the whole contract: score the student's own states, return a provider the chunked loss can slice, and keep the returned targets a normalized distribution.

It asserts what it claims:

teacher: sharpened sharpness 2.0
mean teacher entropy: 1.1773 -> 0.5559 (nats)
sharpened targets stayed normalized and trained without error

Sharpening measurably reduced the teacher's entropy, the K+1 bucket distribution still sums to one to within 1e-5, and a real training step ran against the modified targets.

If you only need to change hyper-parameters, an environment or an objective, you do not need Python at all — edit a recipe. Start from recipes/toy_cpu.yaml, which is commented line by line, and validate it before running:

miniverl validate recipes/toy_cpu.yaml
miniverl train    recipes/toy_cpu.yaml --dry-run