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>

docs/guide/configuration.md

@@ -25,8 +25,8 @@ Benchmark configs inherit from `_base_/default.yaml` and override specific value
 ```yaml
 model:
   backend: azure_openai          # azure_openai | openai_chat | claude_code_exec | qwen
-  teacher: gpt-5.5               # Teacher model (for reflection)
-  student: gpt-5.5               # Student model (for rollout)
+  optimizer: gpt-5.5               # Optimizer model (for reflection)
+  target: gpt-5.5               # Target model (for rollout)
 ```
 
 ### Training

docs/guide/dl-analogy.md

@@ -7,9 +7,9 @@ SkillOpt is designed around a core insight: **optimizing natural-language prompt
 | Deep Learning | SkillOpt | Description |
 |---|---|---|
 | **Model weights** | Skill document (Markdown) | The thing being optimized |
-| **Forward pass** | Rollout | Student executes tasks using current skill |
+| **Forward pass** | Rollout | Target executes tasks using current skill |
 | **Loss function** | Task evaluator | Scores task execution quality |
-| **Backpropagation** | Reflect | Teacher analyzes failures → edit patches |
+| **Backpropagation** | Reflect | Optimizer analyzes failures → edit patches |
 | **Gradients** | Edit patches | Proposed changes to the skill |
 | **Gradient aggregation** | Patch aggregation | Merge similar edits |
 | **Gradient clipping** | Edit selection | Cap max edits per step |
@@ -21,7 +21,7 @@ SkillOpt is designed around a core insight: **optimizing natural-language prompt
 | **Training step** | Step | One rollout → reflect → update cycle |
 | **Epoch** | Epoch | Full pass with slow update + meta memory |
 | **Momentum** | Slow update | Longitudinal comparison at epoch boundary |
-| **Meta-learning** | Meta skill | Cross-epoch teacher strategy memory |
+| **Meta-learning** | Meta skill | Cross-epoch optimizer strategy memory |
 | **Batch size** | `batch_size` | Tasks sampled per rollout |
 | **Data parallelism** | `analyst_workers` | Parallel reflection workers |
 | **Training set** | Train split | Items used for rollout |
@@ -44,7 +44,7 @@ From our experiments, these DL intuitions transfer well:
     - **Cosine schedule > constant** — same as in DL, cosine annealing helps convergence
     - **Moderate LR (4-16) > very high/low** — too few edits = slow learning, too many = noisy
     - **Slow update helps** — longitudinal comparison prevents catastrophic forgetting across epochs
-    - **Meta skill memory improves reflection** — teacher benefits from cross-epoch strategy notes
+    - **Meta skill memory improves reflection** — optimizer benefits from cross-epoch strategy notes
 
 !!! warning "What doesn't transfer"
     - **Batch size ≠ better** — larger rollout batches have diminishing returns due to API costs

docs/guide/first-experiment.md

@@ -33,7 +33,7 @@ optimizer:
   learning_rate: 4        # (max edits per step)
   lr_scheduler: cosine    # (learning rate schedule)
   use_slow_update: true   # (momentum at epoch boundary)
-  use_meta_skill: true    # (cross-epoch teacher memory)
+  use_meta_skill: true    # (cross-epoch optimizer memory)
 
 gradient:
   analyst_workers: 16     # (parallel reflection workers)

docs/guide/new-benchmark.md

@@ -76,7 +76,7 @@ class MyBenchmarkEnv(EnvAdapter):
         Args:
             item: The data item to process
             skill: Current skill document content
-            model: The student model instance
+            model: The target model instance
             
         Returns:
             TaskResult with prediction, score, and trajectory

docs/guide/skill-document.md

@@ -70,7 +70,7 @@ Track your skill's evolution through:
     1. **Start with a seed skill** (`env.skill_init`) if you have domain knowledge — it converges faster
     2. **Use cosine LR schedule** — aggressive early exploration + careful late refinement
     3. **Enable slow update** (`use_slow_update: true`) to prevent forgetting across epochs
-    4. **Enable meta skill** (`use_meta_skill: true`) so the teacher accumulates strategy memory
+    4. **Enable meta skill** (`use_meta_skill: true`) so the optimizer accumulates strategy memory
 
 ## Next Steps
 

docs/guide/training-loop.md

@@ -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