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docs: align benchmark guide and template with dataloader.py naming
The new-benchmark guide and the env template README referred to the data loader file as loader.py, but all six built-in benchmarks name it dataloader.py (skillopt/envs/<name>/dataloader.py). Update the docs and the template rename step to match the actual convention. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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@@ -36,7 +36,7 @@ touch skillopt/envs/docfaithful/__init__.py
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## Step 2 — Implement the data loader
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`skillopt/envs/docfaithful/loader.py`:
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`skillopt/envs/docfaithful/dataloader.py`:
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```python
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from __future__ import annotations
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@@ -165,7 +165,7 @@ import os
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from skillopt.datasets.base import BatchSpec
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from skillopt.envs.base import EnvAdapter
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from skillopt.envs.docfaithful.loader import DocFaithfulDataLoader
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from skillopt.envs.docfaithful.dataloader import DocFaithfulDataLoader
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from skillopt.envs.docfaithful.rollout import run_batch
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from skillopt.gradient.reflect import run_minibatch_reflect
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@@ -21,14 +21,14 @@ This directory provides scaffold files for adding a new benchmark to SkillOpt.
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```bash
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cd skillopt/envs/your_benchmark
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mv env_template.py adapter.py
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mv loader_template.py loader.py
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mv loader_template.py dataloader.py
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```
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…and inside each file rename the classes
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(`TemplateBenchmarkEnv → YourBenchmarkAdapter`,
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`TemplateBenchmarkLoader → YourBenchmarkLoader`)
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and fix the cross-import in `adapter.py`.
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3. **Implement the TODO blocks** inside `adapter.py:rollout` and the
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`_normalize_item` helper in `loader.py`. If you want real reflection,
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`_normalize_item` helper in `dataloader.py`. If you want real reflection,
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uncomment the `run_minibatch_reflect` block in `adapter.py:reflect`.
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4. **Register** the adapter — add a `try / except ImportError` block in
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`scripts/train.py`'s `_register_builtins()` mapping the registry key
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