docs/reference/api.md previously documented a fictional EnvAdapter API
(execute / evaluate / build_prompt + DataItem / TaskResult) and a
BENCHMARK_REGISTRY that never existed in code. Anyone following the
documented contract would hit ImportError or TypeError on the first
instantiation.
Replace both pages with the real shape from skillopt/envs/base.py and
skillopt/datasets/base.py:
- EnvAdapter: build_train_env, build_eval_env, rollout, reflect,
get_task_types (the 5 actual abstract methods).
- Rollout dicts: id / hard / soft required; everything else preserved
into RolloutResult.extras.
- Reflect dicts: {patch, source_type} schema as consumed by
run_minibatch_reflect.
- BatchSpec: slotted-but-mutable dataclass matching the actual
definition (payload defaults to None, metadata to dict()).
- SplitDataLoader.load_split_items as the one mandatory loader method.
- Registry: _ENV_REGISTRY in scripts/train.py (lazy try/except
ImportError block), not a non-existent BENCHMARK_REGISTRY in
skillopt/envs/__init__.py.
- _base_: documented as a string path, since the current YAML loader
only accepts strings.
The new-benchmark.md guide now walks through a docfaithful worked
example with a real rollout helper (chat_target + scorer) instead of
hand-waving over the rollout step. Refs microsoft/SkillOpt#30.
Co-Authored-By: Claude Opus 4 <noreply@anthropic.com>
- Skill optimization framework with training loop analogy
- 11 benchmarks, 4 model backends (Azure OpenAI, Claude, Codex, Qwen)
- WebUI for browser-based training control
- Pluggable architecture for extending benchmarks and backends
