refactor: make EnvAdapter.reflect a shared default (fixes dropped reflect kwargs)

All six adapters duplicated an identical reflect() that delegates to
run_minibatch_reflect. The copies had drifted: OfficeQA/DocVQA silently
dropped meta_skill_context and ALFWorld dropped update_mode, so those
analysts ran without inputs every other benchmark receives (active under
the default use_meta_skill: true).

Move the delegation into EnvAdapter.reflect as one default that forwards
all kwargs uniformly, and delete the six overrides. reflect is no longer
abstract — adapters inherit it and override only for custom logic.

Net -225 lines. Behavior change: OfficeQA/DocVQA/ALFWorld reflect now
receive the kwargs they previously dropped; the three already-correct
benchmarks are unaffected.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

skillopt/envs/_template/README.md

@@ -5,8 +5,8 @@ This directory provides scaffold files for adding a new benchmark to SkillOpt.
 ## Files
 
 - `env_template.py` — Environment adapter template (subclasses
-  `EnvAdapter`; implements the 5 abstract methods so the file is
-  instantiable out of the box).
+  `EnvAdapter`; implements the 4 abstract methods so the file is
+  instantiable out of the box — `reflect` is inherited).
 - `loader_template.py` — Data loader template (subclasses
   `SplitDataLoader`; implements `load_split_items` for `.json`/`.jsonl`).
 - `config_template.yaml` — Config file template.
@@ -28,8 +28,8 @@ This directory provides scaffold files for adding a new benchmark to SkillOpt.
    `TemplateBenchmarkLoader → YourBenchmarkLoader`)
    and fix the cross-import in `adapter.py`.
 3. **Implement the TODO blocks** inside `adapter.py:rollout` and the
-   `_normalize_item` helper in `dataloader.py`. If you want real reflection,
-   uncomment the `run_minibatch_reflect` block in `adapter.py:reflect`.
+   `_normalize_item` helper in `dataloader.py`. (`reflect` is inherited from
+   `EnvAdapter`; override it only for custom reflection logic.)
 4. **Register** the adapter — add a `try / except ImportError` block in
    `scripts/train.py`'s `_register_builtins()` mapping the registry key
    to your `YourBenchmarkAdapter` class. There is no

skillopt/envs/_template/env_template.py

@@ -14,13 +14,9 @@ For a fully worked example see ``skillopt/envs/officeqa/``.
 """
 from __future__ import annotations
 
-import os
-
 from skillopt.datasets.base import BatchSpec
 from skillopt.envs.base import EnvAdapter
 from skillopt.envs._template.loader_template import TemplateBenchmarkLoader
-# When you wire in real reflection, also import:
-# from skillopt.gradient.reflect import run_minibatch_reflect
 
 
 class TemplateBenchmarkEnv(EnvAdapter):
@@ -131,53 +127,12 @@ class TemplateBenchmarkEnv(EnvAdapter):
             )
         return results
 
-    # ── Reflect: turn rollout results into patch dicts ─────────────────
-
-    def reflect(
-        self,
-        results: list[dict],
-        skill_content: str,
-        out_dir: str,
-        **kwargs,
-    ) -> list[dict | None]:
-        """
-        Turn rollouts into a list of raw patch dicts (or None to drop).
-
-        Each non-None dict MUST have:
-          - "patch":       {"edits": [...]}     a Patch.to_dict() payload
-          - "source_type": "failure" | "success"
-
-        Most benchmarks delegate to
-        :func:`skillopt.gradient.reflect.run_minibatch_reflect` which
-        will call the optimizer model with the
-        ``analyst_error_*`` / ``analyst_success_*`` prompts. To enable it,
-        uncomment the import above and call:
-
-            from skillopt.gradient.reflect import run_minibatch_reflect
-            return run_minibatch_reflect(
-                results=results,
-                skill_content=skill_content,
-                prediction_dir=kwargs.get(
-                    "prediction_dir", os.path.join(out_dir, "predictions")
-                ),
-                patches_dir=kwargs.get(
-                    "patches_dir", os.path.join(out_dir, "patches")
-                ),
-                workers=self.analyst_workers,
-                failure_only=self.failure_only,
-                minibatch_size=self.minibatch_size,
-                edit_budget=self.edit_budget,
-                random_seed=kwargs.get("random_seed"),
-                error_system=self.get_error_minibatch_prompt(),
-                success_system=self.get_success_minibatch_prompt(),
-                step_buffer_context=kwargs.get("step_buffer_context", ""),
-                update_mode=getattr(self, "_cfg", {}).get(
-                    "skill_update_mode", "patch"
-                ),
-            )
-        """
-        # Template default: produce no patches (no-op trainer step).
-        return [None for _ in results]
+    # ── Reflect (inherited) ─────────────────────────────────────────────
+    #
+    # ``reflect`` is inherited from ``EnvAdapter``: the default delegates to
+    # ``skillopt.gradient.reflect.run_minibatch_reflect`` using your
+    # ``analyst_error_*`` / ``analyst_success_*`` prompts. You do NOT need to
+    # implement it — override only if your benchmark needs custom reflection.
 
     # ── Stratification hint ────────────────────────────────────────────
 

skillopt/envs/alfworld/adapter.py

@@ -17,7 +17,6 @@ from skillopt.envs.alfworld.rollout import (
     run_alfworld_batch,
     TASKS,
 )
-from skillopt.gradient.reflect import run_minibatch_reflect
 from skillopt.utils import compute_score
 
 
@@ -425,35 +424,5 @@ class ALFWorldAdapter(EnvAdapter):
             all_results.extend(chunk_results)
         return all_results
 
-    def reflect(
-        self,
-        results: list[dict],
-        skill_content: str,
-        out_dir: str,
-        **kwargs,
-    ) -> list[dict | None]:
-        prediction_dir = kwargs.get("prediction_dir", os.path.join(out_dir, "predictions"))
-        patches_dir = kwargs.get("patches_dir", os.path.join(out_dir, "patches"))
-        random_seed = kwargs.get("random_seed")
-        step_buffer_context = kwargs.get("step_buffer_context", "")
-        meta_skill_context = kwargs.get("meta_skill_context", "")
-
-        return run_minibatch_reflect(
-            results=results,
-            skill_content=skill_content,
-            prediction_dir=prediction_dir,
-            patches_dir=patches_dir,
-            workers=self.analyst_workers,
-            failure_only=self.failure_only,
-            minibatch_size=self.minibatch_size,
-            edit_budget=self.edit_budget,
-            random_seed=random_seed,
-            error_system=self.get_error_minibatch_prompt(),
-            success_system=self.get_success_minibatch_prompt(),
-            step_buffer_context=step_buffer_context,
-            meta_skill_context=meta_skill_context,
-        )
-
-
     def get_task_types(self) -> list[str]:
         return list(TASKS)

skillopt/envs/base.py

@@ -231,7 +231,6 @@ class EnvAdapter(ABC):
             (float 0-1). May include env-specific fields.
         """
 
-    @abstractmethod
     def reflect(
         self,
         results: list[dict],
@@ -241,15 +240,36 @@ class EnvAdapter(ABC):
     ) -> list[dict | None]:
         """Analyze rollout results and produce patches.
 
+        Default implementation: delegate to the shared minibatch reflect
+        stage. Every built-in benchmark uses this unchanged — override only
+        if your environment needs custom reflection logic.
+
         Each returned dict conforms to :class:`~skillopt.types.RawPatch`:
         ``"patch"`` (with ``"edits"`` list) + ``"source_type"``
-        (``"failure"`` or ``"success"``).
-
-        Returns
-        -------
-        list[dict | None]
-            Raw analyst outputs; ``None`` entries are filtered out.
+        (``"failure"`` or ``"success"``); ``None`` entries are filtered out.
         """
+        from skillopt.gradient.reflect import run_minibatch_reflect
+
+        return run_minibatch_reflect(
+            results=results,
+            skill_content=skill_content,
+            prediction_dir=kwargs.get(
+                "prediction_dir", os.path.join(out_dir, "predictions")
+            ),
+            patches_dir=kwargs.get(
+                "patches_dir", os.path.join(out_dir, "patches")
+            ),
+            workers=self.analyst_workers,
+            failure_only=self.failure_only,
+            minibatch_size=self.minibatch_size,
+            edit_budget=self.edit_budget,
+            random_seed=kwargs.get("random_seed"),
+            error_system=self.get_error_minibatch_prompt(),
+            success_system=self.get_success_minibatch_prompt(),
+            step_buffer_context=kwargs.get("step_buffer_context", ""),
+            meta_skill_context=kwargs.get("meta_skill_context", ""),
+            update_mode=getattr(self, "_cfg", {}).get("skill_update_mode", "patch"),
+        )
 
     @abstractmethod
     def get_task_types(self) -> list[str]:

skillopt/envs/docvqa/adapter.py

@@ -1,12 +1,9 @@
 from __future__ import annotations
 
-import os
-
 from skillopt.datasets.base import BatchSpec
 from skillopt.envs.base import EnvAdapter
 from skillopt.envs.docvqa.dataloader import DocVQADataLoader
 from skillopt.envs.docvqa.rollout import run_batch
-from skillopt.gradient.reflect import run_minibatch_reflect
 
 
 class DocVQAAdapter(EnvAdapter):
@@ -84,28 +81,6 @@ class DocVQAAdapter(EnvAdapter):
             task_timeout=self.exec_timeout,
         )
 
-    def reflect(self, results: list[dict], skill_content: str, out_dir: str, **kwargs) -> list[dict | None]:
-        prediction_dir = kwargs.get("prediction_dir", os.path.join(out_dir, "predictions"))
-        patches_dir = kwargs.get("patches_dir", os.path.join(out_dir, "patches"))
-        random_seed = kwargs.get("random_seed")
-        step_buffer_context = kwargs.get("step_buffer_context", "")
-        return run_minibatch_reflect(
-            results=results,
-            skill_content=skill_content,
-            prediction_dir=prediction_dir,
-            patches_dir=patches_dir,
-            workers=self.analyst_workers,
-            failure_only=self.failure_only,
-            minibatch_size=self.minibatch_size,
-            edit_budget=self.edit_budget,
-            random_seed=random_seed,
-            error_system=self.get_error_minibatch_prompt(),
-            success_system=self.get_success_minibatch_prompt(),
-            step_buffer_context=step_buffer_context,
-            update_mode=getattr(self, "_cfg", {}).get("skill_update_mode", "patch"),
-        )
-
-
     def get_task_types(self) -> list[str]:
         seen: list[str] = []
         for item in self.dataloader.train_items + self.dataloader.val_items + self.dataloader.test_items:

skillopt/envs/livemathematicianbench/adapter.py

@@ -2,10 +2,8 @@
 from __future__ import annotations
 
 import json
-import os
 
 from skillopt.datasets.base import BatchSpec
-from skillopt.gradient.reflect import run_minibatch_reflect
 from skillopt.envs.base import EnvAdapter
 from skillopt.envs.livemathematicianbench.dataloader import LiveMathematicianBenchDataLoader
 from skillopt.envs.livemathematicianbench.rollout import run_batch
@@ -127,36 +125,5 @@ class LiveMathematicianBenchAdapter(EnvAdapter):
             task_timeout=self.exec_timeout,
         )
 
-    def reflect(
-        self,
-        results: list[dict],
-        skill_content: str,
-        out_dir: str,
-        **kwargs,
-    ) -> list[dict | None]:
-        prediction_dir = kwargs.get("prediction_dir", os.path.join(out_dir, "predictions"))
-        patches_dir = kwargs.get("patches_dir", os.path.join(out_dir, "patches"))
-        random_seed = kwargs.get("random_seed")
-        step_buffer_context = kwargs.get("step_buffer_context", "")
-        meta_skill_context = kwargs.get("meta_skill_context", "")
-
-        return run_minibatch_reflect(
-            results=results,
-            skill_content=skill_content,
-            prediction_dir=prediction_dir,
-            patches_dir=patches_dir,
-            workers=self.analyst_workers,
-            failure_only=self.failure_only,
-            minibatch_size=self.minibatch_size,
-            edit_budget=self.edit_budget,
-            random_seed=random_seed,
-            error_system=self.get_error_minibatch_prompt(),
-            success_system=self.get_success_minibatch_prompt(),
-            step_buffer_context=step_buffer_context,
-            meta_skill_context=meta_skill_context,
-            update_mode=getattr(self, "_cfg", {}).get("skill_update_mode", "patch"),
-        )
-
-
     def get_task_types(self) -> list[str]:
         return self.dataloader.get_task_types()

skillopt/envs/officeqa/adapter.py

@@ -6,7 +6,6 @@ from skillopt.datasets.base import BatchSpec
 from skillopt.envs.base import EnvAdapter
 from skillopt.envs.officeqa.dataloader import OfficeQADataLoader
 from skillopt.envs.officeqa.rollout import run_batch
-from skillopt.gradient.reflect import run_minibatch_reflect
 
 
 class OfficeQAAdapter(EnvAdapter):
@@ -104,28 +103,6 @@ class OfficeQAAdapter(EnvAdapter):
             diagnostic_instruction=kwargs.get("diagnostic_instruction", ""),
         )
 
-    def reflect(self, results: list[dict], skill_content: str, out_dir: str, **kwargs) -> list[dict | None]:
-        prediction_dir = kwargs.get("prediction_dir", os.path.join(out_dir, "predictions"))
-        patches_dir = kwargs.get("patches_dir", os.path.join(out_dir, "patches"))
-        random_seed = kwargs.get("random_seed")
-        step_buffer_context = kwargs.get("step_buffer_context", "")
-        return run_minibatch_reflect(
-            results=results,
-            skill_content=skill_content,
-            prediction_dir=prediction_dir,
-            patches_dir=patches_dir,
-            workers=self.analyst_workers,
-            failure_only=self.failure_only,
-            minibatch_size=self.minibatch_size,
-            edit_budget=self.edit_budget,
-            random_seed=random_seed,
-            error_system=self.get_error_minibatch_prompt(),
-            success_system=self.get_success_minibatch_prompt(),
-            step_buffer_context=step_buffer_context,
-            update_mode=getattr(self, "_cfg", {}).get("skill_update_mode", "patch"),
-        )
-
-
     def get_task_types(self) -> list[str]:
         seen: list[str] = []
         for item in self.dataloader.train_items + self.dataloader.val_items + self.dataloader.test_items:

skillopt/envs/searchqa/adapter.py

@@ -2,13 +2,11 @@
 from __future__ import annotations
 
 import json
-import os
 
 from skillopt.datasets.base import BatchSpec
 from skillopt.envs.base import EnvAdapter
 from skillopt.envs.searchqa.dataloader import SearchQADataLoader
 from skillopt.envs.searchqa.rollout import run_batch
-from skillopt.gradient.reflect import run_minibatch_reflect
 from skillopt.model import get_target_backend
 
 
@@ -94,36 +92,5 @@ class SearchQAAdapter(EnvAdapter):
             task_timeout=self.exec_timeout,
         )
 
-    def reflect(
-        self,
-        results: list[dict],
-        skill_content: str,
-        out_dir: str,
-        **kwargs,
-    ) -> list[dict | None]:
-        prediction_dir = kwargs.get("prediction_dir", os.path.join(out_dir, "predictions"))
-        patches_dir = kwargs.get("patches_dir", os.path.join(out_dir, "patches"))
-        random_seed = kwargs.get("random_seed")
-        step_buffer_context = kwargs.get("step_buffer_context", "")
-        meta_skill_context = kwargs.get("meta_skill_context", "")
-
-        return run_minibatch_reflect(
-            results=results,
-            skill_content=skill_content,
-            prediction_dir=prediction_dir,
-            patches_dir=patches_dir,
-            workers=self.analyst_workers,
-            failure_only=self.failure_only,
-            minibatch_size=self.minibatch_size,
-            edit_budget=self.edit_budget,
-            random_seed=random_seed,
-            error_system=self.get_error_minibatch_prompt(),
-            success_system=self.get_success_minibatch_prompt(),
-            step_buffer_context=step_buffer_context,
-            meta_skill_context=meta_skill_context,
-            update_mode=getattr(self, "_cfg", {}).get("skill_update_mode", "patch"),
-        )
-
-
     def get_task_types(self) -> list[str]:
         return ["qa"]

skillopt/envs/spreadsheetbench/adapter.py

@@ -16,7 +16,6 @@ from skillopt.envs.spreadsheetbench.rollout import (
     run_spreadsheet_batch,
     run_spreadsheet_batch_codegen,
 )
-from skillopt.gradient.reflect import run_minibatch_reflect
 from skillopt.model import get_target_backend, is_target_exec_backend
 
 
@@ -156,37 +155,5 @@ class SpreadsheetBenchAdapter(EnvAdapter):
 
         return results
 
-    def reflect(
-        self,
-        results: list[dict],
-        skill_content: str,
-        out_dir: str,
-        **kwargs,
-    ) -> list[dict | None]:
-        """Analyze rollout results and produce patches (minibatch mode)."""
-        prediction_dir = kwargs.get("prediction_dir", os.path.join(out_dir, "predictions"))
-        patches_dir = kwargs.get("patches_dir", os.path.join(out_dir, "patches"))
-        random_seed = kwargs.get("random_seed")
-        step_buffer_context = kwargs.get("step_buffer_context", "")
-        meta_skill_context = kwargs.get("meta_skill_context", "")
-
-        return run_minibatch_reflect(
-            results=results,
-            skill_content=skill_content,
-            prediction_dir=prediction_dir,
-            patches_dir=patches_dir,
-            workers=self.analyst_workers,
-            failure_only=self.failure_only,
-            minibatch_size=self.minibatch_size,
-            edit_budget=self.edit_budget,
-            random_seed=random_seed,
-            error_system=self.get_error_minibatch_prompt(),
-            success_system=self.get_success_minibatch_prompt(),
-            step_buffer_context=step_buffer_context,
-            meta_skill_context=meta_skill_context,
-            update_mode=getattr(self, "_cfg", {}).get("skill_update_mode", "patch"),
-        )
-
-
     def get_task_types(self) -> list[str]:
         return list(TASK_TYPES)