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>
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Cuzyoung
@@ -15,8 +15,8 @@ Public API
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----------
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- :func:`fmt_trajectory` -- format one conversation into text
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- :func:`fmt_minibatch_trajectories` -- format multiple trajectories for batch analysis
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- :func:`run_error_analyst_minibatch` -- one teacher call for a group of failures
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- :func:`run_success_analyst_minibatch` -- one teacher call for a group of successes
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- :func:`run_error_analyst_minibatch` -- one optimizer call for a group of failures
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- :func:`run_success_analyst_minibatch` -- one optimizer call for a group of successes
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- :func:`run_minibatch_reflect` -- full reflect stage dispatcher
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"""
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from __future__ import annotations
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@@ -27,7 +27,7 @@ import random
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import traceback
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from skillopt.model import chat_teacher
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from skillopt.model import chat_optimizer
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from skillopt.optimizer.meta_skill import format_meta_skill_context
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from skillopt.optimizer.update_modes import (
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get_payload_items,
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@@ -115,7 +115,7 @@ def fmt_minibatch_trajectories(
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``"task_type"``, ``"fail_reason"``, etc. Reads ``conversation.json``
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for each and formats them together with trajectory headers.
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If available, includes the spreadsheet preview and student system prompt
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If available, includes the spreadsheet preview and target system prompt
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so the analyst can see what the agent saw.
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Parameters
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@@ -160,32 +160,32 @@ def fmt_minibatch_trajectories(
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f"{reference_text[:4000]}\n"
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)
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# ── Append student context (what the agent saw) ──────────────
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student_prompt = item.get("student_system_prompt", "")
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if not student_prompt:
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prompt_path = os.path.join(prediction_dir, tid, "student_system_prompt.txt")
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# ── Append target context (what the agent saw) ──────────────
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target_prompt = item.get("target_system_prompt", "")
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if not target_prompt:
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prompt_path = os.path.join(prediction_dir, tid, "target_system_prompt.txt")
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if os.path.exists(prompt_path):
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with open(prompt_path) as f:
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student_prompt = f.read()
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if student_prompt:
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target_prompt = f.read()
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if target_prompt:
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header += (
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f"\n#### Student System Prompt\n"
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f"{student_prompt[:3000]}\n"
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f"\n#### Target System Prompt\n"
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f"{target_prompt[:3000]}\n"
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)
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user_prompt = item.get("student_user_prompt", "")
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user_prompt = item.get("target_user_prompt", "")
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if not user_prompt:
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user_prompt_path = os.path.join(prediction_dir, tid, "student_user_prompt.txt")
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user_prompt_path = os.path.join(prediction_dir, tid, "target_user_prompt.txt")
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if os.path.exists(user_prompt_path):
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with open(user_prompt_path) as f:
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user_prompt = f.read()
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if user_prompt:
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header += (
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f"\n#### Student User Prompt\n"
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f"\n#### Target User Prompt\n"
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f"{user_prompt[:3000]}\n"
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)
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if os.environ.get("REFLACT_CODEX_TRACE_TO_TEACHER", "0") == "1":
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if os.environ.get("REFLACT_CODEX_TRACE_TO_OPTIMIZER", "0") == "1":
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codex_trace_summary = item.get("codex_trace_summary", "")
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if not codex_trace_summary:
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codex_trace_summary_path = os.path.join(prediction_dir, tid, "codex_trace_summary.txt")
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@@ -262,7 +262,7 @@ def run_error_analyst_minibatch(
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meta_skill_context: str = "",
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update_mode: str = "patch",
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) -> dict | None:
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"""Analyze a minibatch of failed trajectories in one teacher call.
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"""Analyze a minibatch of failed trajectories in one optimizer call.
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Parameters
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----------
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@@ -315,13 +315,13 @@ def run_error_analyst_minibatch(
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ctx = f"{ctx}\n{trajectory_memory_context}" if ctx else trajectory_memory_context
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if ctx.strip():
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user += f"## Previous Steps in This Epoch\n{ctx}\n\n"
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teacher_ctx = format_meta_skill_context(meta_skill_context)
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if teacher_ctx:
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user += teacher_ctx + "\n\n"
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optimizer_ctx = format_meta_skill_context(meta_skill_context)
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if optimizer_ctx:
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user += optimizer_ctx + "\n\n"
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user += f"## Failed Trajectories ({len(items)} total)\n{trajectories_text}"
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try:
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response, _ = chat_teacher(
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response, _ = chat_optimizer(
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system=actual_system, user=user,
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max_completion_tokens=64000 if is_full_rewrite_minibatch_mode(mode) else 4096,
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retries=3,
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@@ -350,7 +350,7 @@ def run_success_analyst_minibatch(
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meta_skill_context: str = "",
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update_mode: str = "patch",
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) -> dict | None:
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"""Analyze a minibatch of successful trajectories in one teacher call.
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"""Analyze a minibatch of successful trajectories in one optimizer call.
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Parameters
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----------
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@@ -390,13 +390,13 @@ def run_success_analyst_minibatch(
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ctx = step_buffer_context or trajectory_memory_context or ""
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if ctx.strip():
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user += f"## Previous Steps in This Epoch\n{ctx}\n\n"
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teacher_ctx = format_meta_skill_context(meta_skill_context)
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if teacher_ctx:
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user += teacher_ctx + "\n\n"
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optimizer_ctx = format_meta_skill_context(meta_skill_context)
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if optimizer_ctx:
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user += optimizer_ctx + "\n\n"
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user += f"## Successful Trajectories ({len(items)} total)\n{trajectories_text}"
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try:
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response, _ = chat_teacher(
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response, _ = chat_optimizer(
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system=actual_system, user=user,
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max_completion_tokens=64000 if is_full_rewrite_minibatch_mode(mode) else 4096,
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retries=3,
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@@ -454,7 +454,7 @@ def run_minibatch_reflect(
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meta_skill_context: str = "",
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update_mode: str = "patch",
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) -> list[dict | None]:
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"""Full minibatch reflect stage: group → parallel teacher calls → patches.
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"""Full minibatch reflect stage: group → parallel optimizer calls → patches.
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Separates failure and success trajectories, splits each into minibatches
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of size M, runs all minibatches in parallel, and saves patch files.
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@@ -470,7 +470,7 @@ def run_minibatch_reflect(
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patches_dir : str
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Path to save per-minibatch patch JSON files.
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workers : int
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Max parallel teacher calls.
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Max parallel optimizer calls.
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failure_only : bool
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If True, skip success trajectories.
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minibatch_size : int
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