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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Antigravity Agent
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---
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id: wiki-2026-0508-parameter-sharing
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title: Parameter Sharing
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Weight Sharing, Tied Weights]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [parameter-sharing, weight-tying, cnn, rnn, model-compression]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: pytorch
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---
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# Parameter Sharing
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## 매 한 줄
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> **"매 same weights, different positions"**. 매 single parameter set 가 multiple computations 에 reuse — translation invariance (CNN), temporal invariance (RNN), parameter efficiency (transformer FFN tied embeddings). 매 modern DL 의 fundamental design pattern.
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## 매 핵심
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### 매 motivation
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- Parameter explosion: 매 fully connected layer on image → billions of params.
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- Inductive bias: 매 weight sharing encodes prior (translation/time invariance).
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- Generalization: 매 fewer params → better generalization (less overfit).
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- Compute: 매 shared weights enable convolution / matmul optimization.
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### 매 forms
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- **Spatial sharing (CNN)**: 매 same conv kernel slid across image.
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- **Temporal sharing (RNN/LSTM/GRU)**: 매 same recurrent weights at every timestep.
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- **Cross-layer sharing**: 매 ALBERT, Universal Transformer — 매 same layer params reused L times.
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- **Tied embeddings**: 매 input embedding == output projection (LM head).
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- **Multi-head**: 매 NOT shared (each head has own W_q, W_k, W_v).
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### 매 modern usage
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- ALBERT (2019): cross-layer sharing for BERT compression (12× param reduction).
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- ViT: spatial sharing via patch embedding.
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- Mamba/SSM: temporal sharing via state-space recurrence.
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- LoRA: 매 single low-rank delta shared across positions.
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### 매 응용
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1. CNN image classification (ResNet, ConvNeXt).
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2. Sequence modeling (RNN, Transformer position embeddings).
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3. Model compression (ALBERT, distillation).
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4. Multi-task learning (shared encoder).
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## 💻 패턴
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### CNN spatial sharing
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```python
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import torch.nn as nn
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# Single 3x3 kernel applied to every spatial position
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conv = nn.Conv2d(3, 64, kernel_size=3, padding=1)
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# Params: 3*64*3*3 + 64 = 1792 (independent of image size)
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```
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### Tied input/output embeddings
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```python
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class LanguageModel(nn.Module):
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def __init__(self, vocab_size, dim):
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super().__init__()
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self.embed = nn.Embedding(vocab_size, dim)
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# tie: lm_head.weight = embed.weight
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self.lm_head = nn.Linear(dim, vocab_size, bias=False)
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self.lm_head.weight = self.embed.weight # share!
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def forward(self, x):
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h = self.embed(x)
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return self.lm_head(h) # no extra params
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```
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### Cross-layer sharing (ALBERT-style)
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```python
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class SharedTransformer(nn.Module):
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def __init__(self, num_layers, dim):
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super().__init__()
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self.shared_layer = TransformerBlock(dim) # ONE block
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self.num_layers = num_layers
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def forward(self, x):
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for _ in range(self.num_layers):
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x = self.shared_layer(x) # reuse same params
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return x
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```
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### RNN temporal sharing (built-in)
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```python
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rnn = nn.GRU(input_size=128, hidden_size=256, num_layers=2)
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# At every timestep t, same W_ih, W_hh applied
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# Params independent of sequence length
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```
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### Detect shared params
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```python
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# Count unique parameter tensors
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seen = set()
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unique = 0
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for p in model.parameters():
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if id(p) not in seen:
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seen.add(id(p))
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unique += p.numel()
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print(f"Unique params: {unique}")
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```
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### Multi-task shared encoder
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```python
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class MultiTaskModel(nn.Module):
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def __init__(self):
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super().__init__()
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self.encoder = ResNet50() # SHARED
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self.classifier = nn.Linear(2048, 1000)
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self.detector = DetectionHead(2048)
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def forward(self, x, task):
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features = self.encoder(x)
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return self.classifier(features) if task == "cls" else self.detector(features)
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Image input | CNN spatial sharing |
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| Sequence input | RNN or Transformer (positional sharing) |
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| Memory constrained, many layers | Cross-layer sharing (ALBERT) |
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| LM with large vocab | Tied embeddings (saves vocab*dim params) |
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| Multi-task related | Shared encoder |
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| Tasks unrelated | Don't force sharing — degrades quality |
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**기본값**: tied embeddings + CNN spatial / Transformer positional sharing.
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## 🔗 Graph
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- 부모: [[Inductive-Bias]] · [[LLM_Optimization_and_Deployment_Strategies|Model-Compression]]
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- 응용: [[CNN]] · [[데이터 사이언스 및 ML 엔지니어링|RNN]] · [[Transformer]]
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## 🤖 LLM 활용
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**언제**: 매 designing efficient architecture, debugging param count, applying inductive bias.
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**언제 X**: 매 tasks/positions truly independent (forcing sharing hurts quality).
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## ❌ 안티패턴
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- **Over-sharing**: 매 ALL layers shared → severe quality drop on complex tasks.
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- **No tied embeddings on small LM**: 매 vocab=50k, dim=512 → 25M wasted params.
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- **Sharing across modalities**: 매 vision encoder ≠ text encoder weights (use CLIP-style separate).
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- **Forgetting LayerNorm not shared**: 매 cross-layer share W matrices but keep LN per-layer.
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## 🧪 검증 / 중복
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- Verified (LeCun 1989 CNN, ALBERT paper, Press & Wolf 2017 tied embeddings).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — sharing forms, modern usage, patterns |
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