refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조

에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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+---
+id: wiki-2026-0508-parameter-control
+title: Parameter Control
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [Hyperparameter-Control, Sampler-Control, Adaptive-Parameters]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.85
+verification_status: applied
+tags: [hyperparameter, sampling, control, training, generative-ai]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: python
+  framework: pytorch-vllm
+---
+
+# Parameter Control
+
+## 매 한 줄
+> **"매 fixed config → 매 dynamic schedule"**. Parameter control 은 training / inference / game system 의 knob (learning rate, temperature, difficulty) 을 매 시간/state 에 따라 adaptively 변경하는 전략. 매 generative AI 의 sampler param (temp/top-p/top-k), RL 의 exploration ε, game 의 dynamic balancing 모두 같은 framework.
+
+## 매 핵심
+
+### 매 3 도메인
+1. **ML training**: lr schedule (cosine, warmup), weight decay, dropout 의 adaptive.
+2. **Generative inference**: temperature, top-p, top-k, repetition penalty 의 per-token 조정.
+3. **Game balance**: dynamic difficulty adjustment (DDA), procedural generation 의 parameter.
+
+### 매 control 의 3 mode (Eiben & Smit)
+- **Deterministic**: fixed schedule (cosine LR, ε-decay).
+- **Adaptive**: feedback-based (loss plateau → reduce lr).
+- **Self-adaptive**: parameter 를 의 일부 model state 로 학습 (CMA-ES σ, learnable temperature).
+
+### 매 응용
+1. LLM serving 의 per-request sampler tuning.
+2. RL 의 entropy coefficient 의 auto-tuning (SAC).
+3. Diffusion 의 CFG scale schedule.
+
+## 💻 패턴
+
+### LR scheduler (PyTorch)
+```python
+import torch
+from torch.optim.lr_scheduler import CosineAnnealingLR, LambdaLR
+
+opt = torch.optim.AdamW(model.parameters(), lr=3e-4)
+
+# Warmup + cosine
+def lr_lambda(step):
+    warmup = 1000
+    if step < warmup:
+        return step / warmup
+    progress = (step - warmup) / (TOTAL - warmup)
+    return 0.5 * (1 + math.cos(math.pi * progress))
+
+sched = LambdaLR(opt, lr_lambda)
+```
+
+### vLLM sampler params (per-request)
+```python
+from vllm import LLM, SamplingParams
+
+llm = LLM("meta-llama/Llama-3.3-70B-Instruct")
+
+creative = SamplingParams(temperature=0.9, top_p=0.95, top_k=50,
+                          repetition_penalty=1.1, max_tokens=512)
+factual  = SamplingParams(temperature=0.2, top_p=0.9,  top_k=20,
+                          max_tokens=256)
+
+llm.generate("Write a poem.",  creative)
+llm.generate("Capital of FR?", factual)
+```
+
+### Adaptive temperature (entropy targeting)
+```python
+def adaptive_temp(logits, target_entropy=2.5, iters=10):
+    T = 1.0
+    for _ in range(iters):
+        p = softmax(logits / T)
+        H = -(p * log(p)).sum()
+        T *= (target_entropy / H) ** 0.5
+    return T
+```
+
+### SAC entropy coefficient (self-adaptive)
+```python
+# learnable log_alpha targets a fixed entropy
+log_alpha = torch.zeros(1, requires_grad=True, device=dev)
+alpha_opt = torch.optim.Adam([log_alpha], lr=3e-4)
+target_H = -action_dim   # heuristic
+
+# per gradient step
+alpha = log_alpha.exp()
+loss_alpha = -(log_alpha * (logp.detach() + target_H)).mean()
+alpha_opt.zero_grad(); loss_alpha.backward(); alpha_opt.step()
+```
+
+### Dynamic difficulty (game)
+```python
+class DDA:
+    def __init__(self, target_winrate=0.55, lr=0.05):
+        self.diff = 0.5
+        self.target = target_winrate
+        self.lr = lr
+    def update(self, won: bool):
+        self.diff += self.lr * (int(won) - self.target)
+        self.diff = max(0.0, min(1.0, self.diff))
+```
+
+### CFG scale schedule (diffusion)
+```python
+def cfg_schedule(t, total, base=7.5):
+    # higher CFG early, taper late (Karras schedule trick)
+    progress = t / total
+    return base * (1 - 0.3 * progress)
+```
+
+### Plateau-based lr reduction
+```python
+sched = torch.optim.lr_scheduler.ReduceLROnPlateau(
+    opt, mode="min", factor=0.5, patience=3, min_lr=1e-6)
+for epoch in range(EPOCHS):
+    val_loss = evaluate()
+    sched.step(val_loss)
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| 매 known schedule | deterministic (cosine, exponential) |
+| 매 unknown convergence | adaptive (ReduceLROnPlateau) |
+| 매 RL entropy / GAN balance | self-adaptive (learnable param) |
+| 매 LLM serving | per-request sampler config |
+| 매 game | DDA with target metric |
+
+**기본값**: warmup + cosine for training, temp=0.7/top-p=0.95 for chat LLM.
+
+## 🔗 Graph
+- 부모: [[Hyperparameters|Hyperparameter-Tuning]] · [[Optimization]]
+- 변형: [[Dynamic-Difficulty-Adjustment]]
+- 응용: [[LLM-Sampling]] · [[Reinforcement-Learning]]
+- Adjacent: [[CMA-ES]] · [[Bayesian-Optimization]]
+
+## 🤖 LLM 활용
+**언제**: 매 inference profile 분기 (creative vs factual), per-stage training 의 schedule 추천.
+**언제 X**: 매 first prototype — fixed default 로 시작, control 은 나중.
+
+## ❌ 안티패턴
+- **No warmup**: 매 large LR 로 시작 → loss spike.
+- **Fixed temp for all tasks**: factual 에 0.9, creative 에 0.1 같은 mismatch.
+- **DDA without floor/ceiling**: difficulty 가 unbounded drift.
+- **Self-adaptive without target**: learnable param 이 collapse (alpha → 0).
+
+## 🧪 검증 / 중복
+- Verified (PyTorch 2.5, vLLM 0.6, SAC paper Haarnoja 2018).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — unified ML/inference/game parameter control |