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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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docs/guide/training-loop.md
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@@ -10,8 +10,8 @@ SkillOpt's core insight: **optimizing natural-language skill documents follows t
│ │
│ for epoch in epochs: │
│ for step in steps: │
│ 1. Rollout — Student executes tasks │
│ 2. Reflect — Teacher analyzes trajectories │
│ 1. Rollout — Target executes tasks │
│ 2. Reflect — Optimizer analyzes trajectories │
│ 3. Aggregate — Hierarchical merge of patches │
│ 4. Select — Rank & clip edits (learning rate) │
│ 5. Update — Apply patches to skill doc │
@@ -27,7 +27,7 @@ SkillOpt's core insight: **optimizing natural-language skill documents follows t
### 1. Rollout (Forward Pass)
The **student** model executes tasks using the current skill document as its prompt. Each task produces a trajectory and a score.
The **target** model executes tasks using the current skill document as its prompt. Each task produces a trajectory and a score.
```python
# Analogy: forward pass through the network
@@ -37,7 +37,7 @@ scores = evaluate(predictions, ground_truth)
### 2. Reflect (Backward Pass)
The **teacher** model analyzes failed trajectories and produces **edit patches** — structured suggestions for improving the skill document.
The **optimizer** model analyzes failed trajectories and produces **edit patches** — structured suggestions for improving the skill document.
Two modes:
@@ -84,7 +84,7 @@ At the end of each epoch (starting from epoch 2), the system performs a **longit
### Meta Skill
A **meta-skill memory** accumulates high-level strategy notes across the entire training run. At the end of each epoch, the teacher reflects on what changed between epochs and produces a compact memory that is provided as additional context during future reflection steps.
A **meta-skill memory** accumulates high-level strategy notes across the entire training run. At the end of each epoch, the optimizer reflects on what changed between epochs and produces a compact memory that is provided as additional context during future reflection steps.
## Next Steps