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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
Download Patch File Download Diff File Expand all files Collapse all files
skillopt/gradient/__init__.py
+1 -3
View File
@@ -1,4 +1,4 @@
"""ReflACT Gradient -- trajectory analysis and patch generation.
"""SkillOpt Gradient -- trajectory analysis and patch generation.
Analogous to gradient computation in neural network training: analyzes
minibatch rollout trajectories to produce skill-edit patches (the "gradient"
@@ -8,10 +8,8 @@ Modules
-------
- reflect: minibatch trajectory analysis (gradient computation)
- aggregate: hierarchical patch merging (gradient aggregation)
- deep_probe: diagnostic probe generation (gradient probing)
"""
from skillopt.gradient.reflect import ( # noqa: F401
run_minibatch_reflect,
)
from skillopt.gradient.aggregate import merge_patches # noqa: F401
from skillopt.gradient.deep_probe import generate_deep_probe_instruction # noqa: F401
skillopt/gradient/aggregate.py
+10 -10
View File
@@ -9,7 +9,7 @@ from __future__ import annotations
import json
from concurrent.futures import ThreadPoolExecutor, as_completed
from skillopt.model import chat_teacher
from skillopt.model import chat_optimizer
from skillopt.optimizer.meta_skill import format_meta_skill_context
from skillopt.optimizer.update_modes import (
get_payload_items,
@@ -33,17 +33,17 @@ def _merge_batch(
meta_skill_context: str = "",
level: int = 1,
) -> dict:
"""Call teacher LLM to merge a batch of patches into one."""
"""Call optimizer LLM to merge a batch of patches into one."""
patches_text = json.dumps(patches, ensure_ascii=False, indent=2)
user = (
f"## Current Skill\n{skill_content}\n\n"
f"## Patches to merge ({len(patches)} total, merge level {level})\n{patches_text}"
)
teacher_ctx = format_meta_skill_context(meta_skill_context)
if teacher_ctx:
user = f"{teacher_ctx}\n\n{user}"
optimizer_ctx = format_meta_skill_context(meta_skill_context)
if optimizer_ctx:
user = f"{optimizer_ctx}\n\n{user}"
try:
response, _ = chat_teacher(
response, _ = chat_optimizer(
system=system_prompt,
user=user,
max_completion_tokens=64000 if is_full_rewrite_minibatch_mode(update_mode) else 4096,
@@ -224,11 +224,11 @@ def merge_patches(
f"{len(s_edits)} edits\n\n"
f"{combined_text}"
)
teacher_ctx = format_meta_skill_context(meta_skill_context)
if teacher_ctx:
user = f"{teacher_ctx}\n\n{user}"
optimizer_ctx = format_meta_skill_context(meta_skill_context)
if optimizer_ctx:
user = f"{optimizer_ctx}\n\n{user}"
try:
response, _ = chat_teacher(
response, _ = chat_optimizer(
system=merge_final_prompt,
user=user,
max_completion_tokens=64000 if is_full_rewrite_minibatch_mode(update_mode) else 4096,
skillopt/gradient/reflect.py
+28 -28
View File
@@ -15,8 +15,8 @@ Public API
----------
- :func:`fmt_trajectory` -- format one conversation into text
- :func:`fmt_minibatch_trajectories` -- format multiple trajectories for batch analysis
- :func:`run_error_analyst_minibatch` -- one teacher call for a group of failures
- :func:`run_success_analyst_minibatch` -- one teacher call for a group of successes
- :func:`run_error_analyst_minibatch` -- one optimizer call for a group of failures
- :func:`run_success_analyst_minibatch` -- one optimizer call for a group of successes
- :func:`run_minibatch_reflect` -- full reflect stage dispatcher
"""
from __future__ import annotations
@@ -27,7 +27,7 @@ import random
import traceback
from concurrent.futures import ThreadPoolExecutor, as_completed
from skillopt.model import chat_teacher
from skillopt.model import chat_optimizer
from skillopt.optimizer.meta_skill import format_meta_skill_context
from skillopt.optimizer.update_modes import (
get_payload_items,
@@ -115,7 +115,7 @@ def fmt_minibatch_trajectories(
``"task_type"``, ``"fail_reason"``, etc. Reads ``conversation.json``
for each and formats them together with trajectory headers.
If available, includes the spreadsheet preview and student system prompt
If available, includes the spreadsheet preview and target system prompt
so the analyst can see what the agent saw.
Parameters
@@ -160,32 +160,32 @@ def fmt_minibatch_trajectories(
f"{reference_text[:4000]}\n"
)
# ── Append student context (what the agent saw) ──────────────
student_prompt = item.get("student_system_prompt", "")
if not student_prompt:
prompt_path = os.path.join(prediction_dir, tid, "student_system_prompt.txt")
# ── Append target context (what the agent saw) ──────────────
target_prompt = item.get("target_system_prompt", "")
if not target_prompt:
prompt_path = os.path.join(prediction_dir, tid, "target_system_prompt.txt")
if os.path.exists(prompt_path):
with open(prompt_path) as f:
student_prompt = f.read()
if student_prompt:
target_prompt = f.read()
if target_prompt:
header += (
f"\n#### Student System Prompt\n"
f"{student_prompt[:3000]}\n"
f"\n#### Target System Prompt\n"
f"{target_prompt[:3000]}\n"
)
user_prompt = item.get("student_user_prompt", "")
user_prompt = item.get("target_user_prompt", "")
if not user_prompt:
user_prompt_path = os.path.join(prediction_dir, tid, "student_user_prompt.txt")
user_prompt_path = os.path.join(prediction_dir, tid, "target_user_prompt.txt")
if os.path.exists(user_prompt_path):
with open(user_prompt_path) as f:
user_prompt = f.read()
if user_prompt:
header += (
f"\n#### Student User Prompt\n"
f"\n#### Target User Prompt\n"
f"{user_prompt[:3000]}\n"
)
if os.environ.get("REFLACT_CODEX_TRACE_TO_TEACHER", "0") == "1":
if os.environ.get("REFLACT_CODEX_TRACE_TO_OPTIMIZER", "0") == "1":
codex_trace_summary = item.get("codex_trace_summary", "")
if not codex_trace_summary:
codex_trace_summary_path = os.path.join(prediction_dir, tid, "codex_trace_summary.txt")
@@ -262,7 +262,7 @@ def run_error_analyst_minibatch(
meta_skill_context: str = "",
update_mode: str = "patch",
) -> dict | None:
"""Analyze a minibatch of failed trajectories in one teacher call.
"""Analyze a minibatch of failed trajectories in one optimizer call.
Parameters
----------
@@ -315,13 +315,13 @@ def run_error_analyst_minibatch(
ctx = f"{ctx}\n{trajectory_memory_context}" if ctx else trajectory_memory_context
if ctx.strip():
user += f"## Previous Steps in This Epoch\n{ctx}\n\n"
teacher_ctx = format_meta_skill_context(meta_skill_context)
if teacher_ctx:
user += teacher_ctx + "\n\n"
optimizer_ctx = format_meta_skill_context(meta_skill_context)
if optimizer_ctx:
user += optimizer_ctx + "\n\n"
user += f"## Failed Trajectories ({len(items)} total)\n{trajectories_text}"
try:
response, _ = chat_teacher(
response, _ = chat_optimizer(
system=actual_system, user=user,
max_completion_tokens=64000 if is_full_rewrite_minibatch_mode(mode) else 4096,
retries=3,
@@ -350,7 +350,7 @@ def run_success_analyst_minibatch(
meta_skill_context: str = "",
update_mode: str = "patch",
) -> dict | None:
"""Analyze a minibatch of successful trajectories in one teacher call.
"""Analyze a minibatch of successful trajectories in one optimizer call.
Parameters
----------
@@ -390,13 +390,13 @@ def run_success_analyst_minibatch(
ctx = step_buffer_context or trajectory_memory_context or ""
if ctx.strip():
user += f"## Previous Steps in This Epoch\n{ctx}\n\n"
teacher_ctx = format_meta_skill_context(meta_skill_context)
if teacher_ctx:
user += teacher_ctx + "\n\n"
optimizer_ctx = format_meta_skill_context(meta_skill_context)
if optimizer_ctx:
user += optimizer_ctx + "\n\n"
user += f"## Successful Trajectories ({len(items)} total)\n{trajectories_text}"
try:
response, _ = chat_teacher(
response, _ = chat_optimizer(
system=actual_system, user=user,
max_completion_tokens=64000 if is_full_rewrite_minibatch_mode(mode) else 4096,
retries=3,
@@ -454,7 +454,7 @@ def run_minibatch_reflect(
meta_skill_context: str = "",
update_mode: str = "patch",
) -> list[dict | None]:
"""Full minibatch reflect stage: group → parallel teacher calls → patches.
"""Full minibatch reflect stage: group → parallel optimizer calls → patches.
Separates failure and success trajectories, splits each into minibatches
of size M, runs all minibatches in parallel, and saves patch files.
@@ -470,7 +470,7 @@ def run_minibatch_reflect(
patches_dir : str
Path to save per-minibatch patch JSON files.
workers : int
Max parallel teacher calls.
Max parallel optimizer calls.
failure_only : bool
If True, skip success trajectories.
minibatch_size : int