refactor: rename teacher/student to optimizer/target, remove best skills, fix slow update

- Rename teacher -> optimizer, student -> target across all code, configs, docs, prompts
- CLI: --teacher_model -> --optimizer_model, --student_model -> --target_model
- Remove best_skill files, keep only initial skills
- Fix slow update gate (force write into skill)
- Fix SLOW_UPDATE marker stripping
- Remove deep_reflect and meta_reflect mechanisms
- Update .env.example with export prefix and azure_cli docs
- Add endpoint empty validation in azure_openai.py

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

skillopt/envs/docvqa/adapter.py

@@ -4,7 +4,6 @@ import os
 
 from skillopt.datasets.base import BatchSpec
 from skillopt.envs.base import EnvAdapter
-from skillopt.envs.deep_reflect import run_no_reference_deep_reflect
 from skillopt.envs.docvqa.dataloader import DocVQADataLoader
 from skillopt.envs.docvqa.rollout import run_batch
 from skillopt.gradient.reflect import run_minibatch_reflect
@@ -28,11 +27,7 @@ class DocVQAAdapter(EnvAdapter):
         edit_budget: int = 4,
         seed: int = 42,
         limit: int = 0,
-        image_detail: str = "auto",
-        use_deep_reflect: bool = False,
-        deep_reflect_failures: int = 4,
-        deep_reflect_successes: int = 2,
-    ) -> None:
+        image_detail: str = "auto",    ) -> None:
         self.max_turns = max_turns
         self.exec_timeout = exec_timeout
         self.workers = workers
@@ -41,9 +36,6 @@ class DocVQAAdapter(EnvAdapter):
         self.minibatch_size = minibatch_size
         self.edit_budget = edit_budget
         self.image_detail = image_detail
-        self.use_deep_reflect = use_deep_reflect
-        self.deep_reflect_failures = deep_reflect_failures
-        self.deep_reflect_successes = deep_reflect_successes
         self.dataloader = DocVQADataLoader(
             split_dir=split_dir,
             data_path=data_path,
@@ -109,38 +101,6 @@ class DocVQAAdapter(EnvAdapter):
             update_mode=getattr(self, "_cfg", {}).get("skill_update_mode", "patch"),
         )
 
-    def deep_reflect(
-        self,
-        results: list[dict],
-        skill_content: str,
-        out_dir: str,
-        **kwargs,
-    ) -> list[dict | None]:
-        return run_no_reference_deep_reflect(
-            self,
-            results,
-            skill_content,
-            out_dir,
-            env_manager=kwargs.get("env_manager"),
-            prediction_dir=kwargs.get("prediction_dir"),
-            random_seed=kwargs.get("random_seed"),
-            step_buffer_context=kwargs.get("step_buffer_context", ""),
-            output_requirements=[
-                "- There is no hidden reference block. Use only the document image prompt, student output, and evaluation result to infer what intermediate state is worth probing.",
-                "- The instruction must explicitly request a short <analysis>...</analysis> block before the final <answer>...</answer>.",
-                "- The readout should focus on visual region, field/table/figure label, OCR text read, candidate answer, and answer-format normalization.",
-                "- Do not ask for exhaustive transcription or a full chain-of-thought.",
-                "- The instruction text should be ready to append directly to the student's prompt.",
-            ],
-            metadata_builder=lambda item: {
-                "id": str(item.get("id")),
-                "task_type": str(item.get("task_type") or "docvqa"),
-                "question_preview": str(item.get("question") or "")[:200],
-                "image_path": item.get("image_path", ""),
-                "docId": item.get("docId", ""),
-                "page": item.get("ucsf_document_page_no", ""),
-            },
-        )
 
     def get_task_types(self) -> list[str]:
         seen: list[str] = []

skillopt/envs/docvqa/rollout.py

@@ -6,8 +6,8 @@ import time
 from concurrent.futures import FIRST_COMPLETED, ThreadPoolExecutor, wait
 
 from skillopt.envs.docvqa.evaluator import evaluate
-from skillopt.model import chat_student_messages, get_student_backend, is_student_exec_backend
-from skillopt.model.codex_harness import prepare_workspace, render_skill_md, run_student_exec
+from skillopt.model import chat_target_messages, get_target_backend, is_target_exec_backend
+from skillopt.model.codex_harness import prepare_workspace, render_skill_md, run_target_exec
 from skillopt.prompts import load_prompt
 
 
@@ -112,11 +112,11 @@ def _run_codex_once(
         images=[item["image_path"]],
     )
     prompt = (
-        "Use the `skillopt-student` skill available in this workspace.\n"
+        "Use the `skillopt-target` skill available in this workspace.\n"
         "Read `task.md`, inspect the attached document image, and answer the DocVQA question.\n"
         "Return the final answer inside <answer>...</answer>."
     )
-    final_message, raw = run_student_exec(
+    final_message, raw = run_target_exec(
         work_dir=work_dir,
         prompt=prompt,
         model=model,
@@ -158,7 +158,7 @@ def process_one(
         system_prompt = ""
         user_text = ""
         conversation: list[dict] = []
-        if is_student_exec_backend():
+        if is_target_exec_backend():
             from skillopt.model import azure_openai as _llm
 
             conversation = [
@@ -172,7 +172,7 @@ def process_one(
                     pred_dir=os.path.join(out_root, "predictions", item_id),
                     item=item,
                     skill_content=skill_content,
-                    model=_llm.STUDENT_DEPLOYMENT,
+                    model=_llm.TARGET_DEPLOYMENT,
                     timeout=exec_timeout,
                     image_detail=image_detail,
                     diagnostic_mode=diagnostic_mode if turn == 0 else False,
@@ -198,7 +198,7 @@ def process_one(
             ]
             for turn in range(max_turns):
                 if turn == 0:
-                    resp_text, _ = chat_student_messages(
+                    resp_text, _ = chat_target_messages(
                         messages=messages,
                         max_completion_tokens=768,
                         retries=5,
@@ -212,7 +212,7 @@ def process_one(
                         {"role": "assistant", "content": response},
                         {"role": "user", "content": "Review the same image carefully and answer again. Keep the final answer inside <answer>...</answer>."},
                     ]
-                    resp_text, _ = chat_student_messages(
+                    resp_text, _ = chat_target_messages(
                         messages=refinement_messages,
                         max_completion_tokens=512,
                         retries=5,
@@ -230,9 +230,9 @@ def process_one(
 
         pred_dir = os.path.join(out_root, "predictions", item_id)
         os.makedirs(pred_dir, exist_ok=True)
-        with open(os.path.join(pred_dir, "student_system_prompt.txt"), "w", encoding="utf-8") as f:
+        with open(os.path.join(pred_dir, "target_system_prompt.txt"), "w", encoding="utf-8") as f:
             f.write(system_prompt)
-        with open(os.path.join(pred_dir, "student_user_prompt.txt"), "w", encoding="utf-8") as f:
+        with open(os.path.join(pred_dir, "target_user_prompt.txt"), "w", encoding="utf-8") as f:
             f.write(user_text)
 
         eval_result = evaluate(response, item.get("answers", []))