fix: rename remaining teacher/student refs, remove .gradio from repo

- Fix teacher/student in deep_reflect, meta_reflect, sealqa, babyvision,
  mathverse, mmrb, swebench envs and prompt templates
- Remove .gradio/certificate.pem from tracked files
- Add .gradio/ to .gitignore

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

Author: Cuzyoung

.gitignore

@@ -39,3 +39,4 @@ docs/reflact_conda_env_export.yml
 docs/reflact_overview.html
 docs/render_ablation_paper_tables.py
 docs/让*
+.gradio/

.gradio/certificate.pem

@@ -1,20 +1,20 @@
 You are an expert diagnostic-probe designer for ALFWorld embodied tasks.
 
-You will design one short diagnostic instruction to append to the student's prompt
+You will design one short diagnostic instruction to append to the target's prompt
 for a handful of representative ALFWorld trajectories.
 
-The goal is to expose whether the student has the right intermediate subgoal,
+The goal is to expose whether the target has the right intermediate subgoal,
 object/receptacle state, and next-step intention without substantially changing
 the current scaffold.
 
 ## Hard Constraints
-1. Do NOT substantially change the student's existing action-selection scaffold.
+1. Do NOT substantially change the target's existing action-selection scaffold.
 2. Do NOT prescribe a brand-new planner or long multi-step policy.
 3. Do NOT ask for exhaustive search over all objects or all admissible actions.
 4. Keep the diagnostic readout brief and place it inside the existing <think>...</think> block.
-5. The student must still output exactly one admissible action inside <action>...</action>.
+5. The target must still output exactly one admissible action inside <action>...</action>.
 6. If hidden reference material is provided, use it only to target the right latent gap.
-7. Never copy hidden reference content into the student-facing probe.
+7. Never copy hidden reference content into the target-facing probe.
 
 ## Good Probe Targets
 - current subgoal
@@ -31,5 +31,5 @@ the current scaffold.
 Respond ONLY with a valid JSON object:
 {
   "reasoning": "<why this probe reveals the latent skill gap>",
-  "probe_instruction": "<the exact instruction text to append to the student prompt>"
+  "probe_instruction": "<the exact instruction text to append to the target prompt>"
 }

skillopt/envs/alfworld/prompts/deep_probe.md

@@ -10,7 +10,7 @@
 from skillopt.envs.base import EnvAdapter
 from skillopt.envs.babyvision.dataloader import BabyVisionDataLoader
 from skillopt.envs.babyvision.rollout import run_batch
-from skillopt.model import get_student_backend
+from skillopt.model import get_target_backend
 
 
 class BabyVisionAdapter(EnvAdapter):
@@ -165,7 +165,7 @@ def deep_reflect(
         random_seed = kwargs.get("random_seed")
         step_buffer_context = kwargs.get("step_buffer_context", "")
         meta_skill_context = kwargs.get("meta_skill_context", "")
-        codex_backend = get_student_backend() == "codex_exec"
+        codex_backend = get_target_backend() == "codex_exec"
         selected_items = self.select_representative_items(
             results,
             env_manager if isinstance(env_manager, list) else None,

skillopt/envs/babyvision/adapter.py

@@ -1,14 +1,14 @@
 You are an expert diagnostic-probe designer for BabyVision-style visual reasoning tasks.
 
-You will be shown representative trajectories, the current student skill, and the student's original prompt context.
-Design one SMALL diagnostic instruction that exposes the student's intermediate visual judgment without materially changing the original scaffold.
+You will be shown representative trajectories, the current target skill, and the target's original prompt context.
+Design one SMALL diagnostic instruction that exposes the target's intermediate visual judgment without materially changing the original scaffold.
 
 ## Hard Constraints
 1. Do NOT substantially change the original scaffold.
 2. Do NOT prescribe a new step-by-step solving method.
 3. You MAY ask for a short structured list of a few intermediate conclusions, candidate cues, or counted units, as long as it stays close to the original scaffold.
 4. Do NOT ask for exhaustive listing of all cells, all objects, or a full chain-of-thought.
-5. Ask only for a short readout that reveals the student's current latent state.
+5. Ask only for a short readout that reveals the target's current latent state.
 6. Keep it brief and structured, and require the final answer to remain in <answer>...</answer>.
 
 ## Good Probe Targets
@@ -21,5 +21,5 @@ Design one SMALL diagnostic instruction that exposes the student's intermediate
 Respond ONLY with a valid JSON object:
 {
   "reasoning": "<why this probe is informative>",
-  "probe_instruction": "<the exact instruction text to append to the student prompt>"
+  "probe_instruction": "<the exact instruction text to append to the target prompt>"
 }

skillopt/envs/babyvision/prompts/deep_probe.md

@@ -8,8 +8,8 @@
 from concurrent.futures import ThreadPoolExecutor, as_completed
 
 from skillopt.envs.babyvision.evaluator import evaluate_item, evaluation_mode, extract_boxed_answer
-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
 
 def _build_system(skill_content: str) -> str:
@@ -137,11 +137,11 @@ 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 image, and answer the question.\n"
         "Return the final answer in \\boxed{...}."
     )
-    final_message, raw = run_student_exec(
+    final_message, raw = run_target_exec(
         work_dir=work_dir,
         prompt=prompt,
         model=model,
@@ -195,7 +195,7 @@ def process_one(
         pred_dir = os.path.join(out_root, "predictions", item_id)
         os.makedirs(pred_dir, exist_ok=True)
 
-        if is_student_exec_backend():
+        if is_target_exec_backend():
             from skillopt.model import azure_openai as _llm
 
             response = ""
@@ -209,7 +209,7 @@ def process_one(
                     pred_dir=pred_dir,
                     item=item,
                     skill_content=skill_content,
-                    model=_llm.STUDENT_DEPLOYMENT,
+                    model=_llm.TARGET_DEPLOYMENT,
                     timeout=120,
                     image_detail=image_detail,
                     diagnostic_mode=diagnostic_mode if turn == 0 else False,
@@ -224,9 +224,9 @@ def process_one(
             result["response"] = response
             result["agent_ok"] = True
             result["n_turns"] = len(conversation) - 1
-            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_item(
@@ -299,7 +299,7 @@ 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,
@@ -317,7 +317,7 @@ def process_one(
                     {"role": "assistant", "content": response},
                     {"role": "user", "content": refinement_text},
                 ]
-                resp_text, _ = chat_student_messages(
+                resp_text, _ = chat_target_messages(
                     messages=refinement_messages,
                     max_completion_tokens=512,
                     retries=5,
@@ -332,9 +332,9 @@ def process_one(
         result["agent_ok"] = True
         result["n_turns"] = len(conversation) - 1
 
-        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_item(

skillopt/envs/babyvision/rollout.py

@@ -21,7 +21,7 @@ def run_no_reference_deep_reflect(
     output_requirements: list[str] | None = None,
     metadata_builder: Callable[[dict], dict] | None = None,
 ) -> list[dict | None]:
-    """Run teacher-designed diagnostic probing without hidden references."""
+    """Run optimizer-designed diagnostic probing without hidden references."""
     if not getattr(adapter, "use_deep_reflect", False):
         return []
     if not isinstance(env_manager, list):

skillopt/envs/deep_reflect.py

@@ -1,13 +1,13 @@
 You are an expert diagnostic-probe designer for theorem-grounded mathematical multiple-choice tasks.
 
-You will be shown representative trajectories, the current student skill, and the student's original prompt context.
-Design one SMALL diagnostic instruction that exposes the student's intermediate judgment without materially changing the original scaffold.
+You will be shown representative trajectories, the current target skill, and the target's original prompt context.
+Design one SMALL diagnostic instruction that exposes the target's intermediate judgment without materially changing the original scaffold.
 
 ## Hard Constraints
 1. Do NOT substantially change the original scaffold.
 2. Do NOT prescribe a new multi-step theorem-solving procedure.
 3. Do NOT ask for a full proof, full chain-of-thought, or exhaustive option-by-option derivation.
-4. Ask only for a short readout of the signals already behind the student's current answer.
+4. Ask only for a short readout of the signals already behind the target's current answer.
 5. Keep it brief and structured, and require the final answer to remain in <answer>...</answer>.
 
 ## Good Probe Targets
@@ -19,5 +19,5 @@ Design one SMALL diagnostic instruction that exposes the student's intermediate
 Respond ONLY with a valid JSON object:
 {
   "reasoning": "<why this probe is informative>",
-  "probe_instruction": "<the exact instruction text to append to the student prompt>"
+  "probe_instruction": "<the exact instruction text to append to the target prompt>"
 }

skillopt/envs/livemathematicianbench/prompts/deep_probe.md

@@ -1,26 +1,26 @@
 You are an expert diagnostic-probe designer for theorem-grounded mathematical multiple-choice tasks executed through a Codex trace.
 
-You will be shown representative trajectories, the current student skill, the student's original prompt context, hidden reference fields, and numbered Codex trace steps.
-Choose exactly one trajectory and one probe point. The probe point determines how much of the prior Codex trace will be shown back to the student before asking a short diagnostic question.
+You will be shown representative trajectories, the current target skill, the target's original prompt context, hidden reference fields, and numbered Codex trace steps.
+Choose exactly one trajectory and one probe point. The probe point determines how much of the prior Codex trace will be shown back to the target before asking a short diagnostic question.
 
 ## Hard Constraints
-1. Do NOT reveal or paraphrase the hidden reference directly to the student.
+1. Do NOT reveal or paraphrase the hidden reference directly to the target.
 2. Do NOT prescribe a new full solving procedure.
 3. Do NOT ask for a full proof, full chain-of-thought, or exhaustive option-by-option derivation.
-4. Ask only for a short readout of the signal that should already exist at that point in the student's process.
+4. Ask only for a short readout of the signal that should already exist at that point in the target's process.
 5. The probe instruction must explicitly request a short <analysis>...</analysis> block before the final <answer>...</answer>.
 6. Select a probe point that is informative about theorem choice, decisive constraint, option elimination, or why a stronger/weaker option should be rejected.
 
 ## Probe Point Semantics
 - `probe_target_id` must be one of the shown trajectory ids.
-- `probe_after_step` is the last numbered Codex trace step that should remain in the student's context.
-- The student will be re-run with the raw trace up to and including `probe_after_step`, then asked your `probe_instruction`.
+- `probe_after_step` is the last numbered Codex trace step that should remain in the target's context.
+- The target will be re-run with the raw trace up to and including `probe_after_step`, then asked your `probe_instruction`.
 - To probe before a tool call, choose the step immediately before that tool call.
 
 Respond ONLY with a valid JSON object:
 {
-  "reasoning": "<why this trajectory and probe point expose the student's intermediate state>",
+  "reasoning": "<why this trajectory and probe point expose the target's intermediate state>",
   "probe_target_id": "<trajectory id>",
   "probe_after_step": <integer step number>,
-  "probe_instruction": "<the exact instruction text to append to the student's prompt>"
+  "probe_instruction": "<the exact instruction text to append to the target's prompt>"
 }

skillopt/envs/livemathematicianbench/prompts/deep_probe_codex.md

@@ -10,7 +10,7 @@
 from skillopt.envs.mathverse.rollout import run_batch
 from skillopt.gradient.deep_probe import generate_deep_probe_instruction
 from skillopt.gradient.reflect import run_minibatch_reflect
-from skillopt.model import get_student_backend
+from skillopt.model import get_target_backend
 
 
 class MathVerseAdapter(EnvAdapter):
@@ -176,7 +176,7 @@ def deep_reflect(
         selected_ids = {str(item["id"]) for item in selected_items}
         selected_results = [row for row in results if str(row.get("id")) in selected_ids]
         selected_examples = self.attach_reference_context(selected_results, selected_items)
-        codex_backend = get_student_backend() == "codex_exec"
+        codex_backend = get_target_backend() == "codex_exec"
         if codex_backend:
             selected_examples = self.attach_codex_probe_context(selected_examples, prediction_dir)
         selected_metadata = []