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

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- 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>
This commit is contained in:
Cuzyoung
2026-05-24 19:15:03 +00:00
parent 6e165d5347
commit 4a1b984d87
70 changed files with 1083 additions and 2068 deletions
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skillopt/envs/livemathematicianbench/rollout.py
+12 -12
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@@ -7,8 +7,8 @@ import time
from concurrent.futures import FIRST_COMPLETED, ThreadPoolExecutor, wait
from skillopt.envs.livemathematicianbench.evaluator import evaluate
from skillopt.model import chat_student, 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, 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:
@@ -95,11 +95,11 @@ def _run_codex_once(
work_dir = os.path.join(pred_dir, "codex_exec")
prepare_workspace(work_dir=work_dir, skill_md=skill_md, task_text=task_text)
prompt = (
"Use the `skillopt-student` skill available in this workspace.\n"
"Use the `skillopt-target` skill available in this workspace.\n"
"Read `task.md` and solve the multiple-choice problem.\n"
"Output only the final choice label inside <answer>...</answer>."
)
final_message, raw = run_student_exec(
final_message, raw = run_target_exec(
work_dir=work_dir,
prompt=prompt,
model=model,
@@ -143,7 +143,7 @@ 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
conversation: list[dict] = []
@@ -155,7 +155,7 @@ def process_one(
pred_dir=pred_dir,
skill_content=skill_content,
item=item,
model=_llm.STUDENT_DEPLOYMENT,
model=_llm.TARGET_DEPLOYMENT,
timeout=exec_timeout,
use_theorem=use_theorem,
use_sketch=use_sketch,
@@ -172,9 +172,9 @@ def process_one(
result["agent_ok"] = True
result["n_turns"] = len(conversation)
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)
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)
eval_result = evaluate(response, item["correct_choice"], item["choices"])
@@ -216,7 +216,7 @@ def process_one(
for turn in range(max_turns):
if turn == 0:
resp_text, _ = chat_student(
resp_text, _ = chat_target(
system=system,
user=user,
max_completion_tokens=16384,
@@ -230,7 +230,7 @@ def process_one(
"Re-evaluate the exact option wording. If needed, correct it. "
"Output only the final choice label inside <answer>...</answer>."
)
resp_text, _ = chat_student(
resp_text, _ = chat_target(
system=system,
user=refinement,
max_completion_tokens=16384,
@@ -247,9 +247,9 @@ def process_one(
result["agent_ok"] = True
result["n_turns"] = len(conversation)
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)
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)
eval_result = evaluate(response, item["correct_choice"], item["choices"])