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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/spreadsheetbench/rollout.py
+32 -32
View File
@@ -233,37 +233,37 @@ def process_one(
no1, ip1, _ = cases[0]
pred_path_1 = os.path.join(task_out_dir, f"{no1}_pred.xlsx")
student_prompt_parts = [
target_prompt_parts = [
f"# Instruction\n{instruction}",
f"# Input file\n{ip1}",
f"# Output file\n{pred_path_1}",
]
if instruction_type:
student_prompt_parts.append(f"# Instruction type\n{instruction_type}")
target_prompt_parts.append(f"# Instruction type\n{instruction_type}")
if answer_position_eval:
student_prompt_parts.append(f"# Answer position\n{answer_position_eval}")
target_prompt_parts.append(f"# Answer position\n{answer_position_eval}")
if diagnostic_trace_context.strip():
student_prompt_parts.insert(
target_prompt_parts.insert(
0,
"# Previous Codex Trace Snapshot\n"
"This is a partial transcript from an earlier attempt. Use it as your current reasoning context.\n\n"
f"{diagnostic_trace_context.strip()}",
)
if diagnostic_mode and diagnostic_instruction.strip():
student_prompt_parts.append(f"# Training readout\n{diagnostic_instruction.strip()}")
student_user_prompt = "\n\n".join(student_prompt_parts)
target_prompt_parts.append(f"# Training readout\n{diagnostic_instruction.strip()}")
target_user_prompt = "\n\n".join(target_prompt_parts)
try:
from skillopt.envs.spreadsheetbench.react_agent import _build_system
student_system_prompt = _build_system(skill_content)
target_system_prompt = _build_system(skill_content)
except Exception:
student_system_prompt = ""
if student_system_prompt:
with open(os.path.join(task_out_dir, "student_system_prompt.txt"), "w") as f:
f.write(student_system_prompt)
result["student_system_prompt"] = student_system_prompt
with open(os.path.join(task_out_dir, "student_user_prompt.txt"), "w") as f:
f.write(student_user_prompt)
result["student_user_prompt"] = student_user_prompt
target_system_prompt = ""
if target_system_prompt:
with open(os.path.join(task_out_dir, "target_system_prompt.txt"), "w") as f:
f.write(target_system_prompt)
result["target_system_prompt"] = target_system_prompt
with open(os.path.join(task_out_dir, "target_user_prompt.txt"), "w") as f:
f.write(target_user_prompt)
result["target_user_prompt"] = target_user_prompt
# ── Stage 1: run ReAct agent on test case 1 ─────────────────────
result["phase"] = "agent"
@@ -288,14 +288,14 @@ def process_one(
diagnostic_trace_context=diagnostic_trace_context,
)
result["n_turns"] = agent_result.get("n_turns", 0)
if agent_result.get("student_system_prompt"):
with open(os.path.join(task_out_dir, "student_system_prompt.txt"), "w") as f:
f.write(agent_result["student_system_prompt"])
result["student_system_prompt"] = agent_result["student_system_prompt"]
if agent_result.get("student_user_prompt"):
with open(os.path.join(task_out_dir, "student_user_prompt.txt"), "w") as f:
f.write(agent_result["student_user_prompt"])
result["student_user_prompt"] = agent_result["student_user_prompt"]
if agent_result.get("target_system_prompt"):
with open(os.path.join(task_out_dir, "target_system_prompt.txt"), "w") as f:
f.write(agent_result["target_system_prompt"])
result["target_system_prompt"] = agent_result["target_system_prompt"]
if agent_result.get("target_user_prompt"):
with open(os.path.join(task_out_dir, "target_user_prompt.txt"), "w") as f:
f.write(agent_result["target_user_prompt"])
result["target_user_prompt"] = agent_result["target_user_prompt"]
# Save conversation log
with open(os.path.join(task_out_dir, "conversation.json"), "w") as f:
@@ -606,7 +606,7 @@ def process_one_codegen(
task_out_dir = os.path.join(out_root, "predictions", task_id)
os.makedirs(task_out_dir, exist_ok=True)
# ── Save context for Teacher (Reflect stage) ──────────────────
# ── Save context for Optimizer (Reflect stage) ──────────────────
from skillopt.envs.spreadsheetbench.codegen_agent import (
_preview_workbook, _build_system, _build_user,
)
@@ -615,8 +615,8 @@ def process_one_codegen(
preview_text = _preview_workbook(first_input_for_preview)
except Exception:
preview_text = "(preview failed)"
student_system = _build_system(skill_content)
student_user = _build_user(
target_system = _build_system(skill_content)
target_user = _build_user(
instruction,
first_input_for_preview,
instruction_type,
@@ -628,14 +628,14 @@ def process_one_codegen(
with open(os.path.join(task_out_dir, "spreadsheet_preview.txt"), "w") as f:
f.write(preview_text)
with open(os.path.join(task_out_dir, "student_system_prompt.txt"), "w") as f:
f.write(student_system)
with open(os.path.join(task_out_dir, "student_user_prompt.txt"), "w") as f:
f.write(student_user)
with open(os.path.join(task_out_dir, "target_system_prompt.txt"), "w") as f:
f.write(target_system)
with open(os.path.join(task_out_dir, "target_user_prompt.txt"), "w") as f:
f.write(target_user)
result["spreadsheet_preview"] = preview_text
result["student_system_prompt"] = student_system
result["student_user_prompt"] = student_user
result["target_system_prompt"] = target_system
result["target_user_prompt"] = target_user
# ── LLM phase ──────────────────────────────────────────────────
result["phase"] = "llm"