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>

skillopt/envs/babyvision/adapter.py

@@ -10,7 +10,7 @@ from skillopt.gradient.reflect import run_minibatch_reflect
 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 @@ class BabyVisionAdapter(EnvAdapter):
         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/prompts/deep_probe.md

@@ -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/rollout.py

@@ -8,8 +8,8 @@ import os
 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(