25. Research Frontiers
25. Research Frontiers¶
๊ฐ์¶
Foundation Model ์ฐ๊ตฌ์ ์ต์ ์ ์ ๋ค๋ฃน๋๋ค. World Models, o1-style Reasoning, Synthetic Data, Multi-Agent ์์คํ ๋ฑ ๋ฏธ๋ ๋ฐฉํฅ์ ํ๊ตฌํฉ๋๋ค.
1. o1-style Reasoning (Test-time Compute)¶
1.1 ๊ฐ๋ ¶
๊ธฐ์กด LLM vs o1-style:
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ ๊ธฐ์กด LLM: โ
โ - ํ์ต ์๊ฐ์ ๊ณ์ฐ ์ง์ค (๋ ํฐ ๋ชจ๋ธ, ๋ ๋ง์ ๋ฐ์ดํฐ) โ
โ - ์ถ๋ก ์ ๊ณ ์ ๋ forward pass โ
โ - ๋ณต์กํ ๋ฌธ์ ์ ํ๊ณ โ
โ โ
โ o1-style (Test-time Compute Scaling): โ
โ - ์ถ๋ก ์ ๋ ๋ง์ ๊ณ์ฐ ์ฌ์ฉ โ
โ - Chain-of-Thought ์๋ ์์ฑ โ
โ - ์ฌ๋ฌ ๊ฒฝ๋ก ํ์ ํ ์ต์ ์ ํ โ
โ - ๋ฌธ์ ๋์ด๋์ ๋ฐ๋ผ ์ ์์ ๊ณ์ฐ โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
ํต์ฌ ๊ธฐ๋ฒ:
1. Internal Chain-of-Thought
2. Search/Verification loops
3. Self-consistency checking
4. Reward model guided search
1.2 ๊ฐ๋ ์ ๊ตฌํ¶
import torch
from typing import List, Tuple
class ReasoningModel:
"""o1-style ์ถ๋ก ๋ชจ๋ธ (๊ฐ๋
์ ๊ตฌํ)"""
def __init__(self, base_model, reward_model):
self.model = base_model
self.reward_model = reward_model
def reason(
self,
problem: str,
max_thinking_tokens: int = 10000,
num_candidates: int = 5
) -> str:
"""ํ์ฅ๋ ์ถ๋ก """
# 1. ์ฌ๋ฌ reasoning chain ์์ฑ
candidates = self._generate_candidates(problem, num_candidates)
# 2. ๊ฐ chain ํ๊ฐ
scored_candidates = []
for chain, answer in candidates:
score = self._evaluate_chain(chain, answer)
scored_candidates.append((chain, answer, score))
# 3. ์ต์ ์ ๋ต๋ณ ์ ํ
best = max(scored_candidates, key=lambda x: x[2])
return best[1] # ๋ต๋ณ๋ง ๋ฐํ (chain์ ๋ด๋ถ)
def _generate_candidates(
self,
problem: str,
n: int
) -> List[Tuple[str, str]]:
"""์ฌ๋ฌ ์ถ๋ก ๊ฒฝ๋ก ์์ฑ"""
candidates = []
for _ in range(n):
# Step-by-step reasoning ์์ฑ
chain = self._generate_reasoning_chain(problem)
# Chain์์ ์ต์ข
๋ต๋ณ ์ถ์ถ
answer = self._extract_answer(chain)
candidates.append((chain, answer))
return candidates
def _generate_reasoning_chain(self, problem: str) -> str:
"""์ถ๋ก ์ฒด์ธ ์์ฑ"""
prompt = f"""Solve this problem step by step.
Think carefully and show your reasoning.
Problem: {problem}
Let me think through this carefully..."""
# ๊ธธ์ด ์ ํ ์์ด ์์ฑ (๋๋ ๋งค์ฐ ๊ธด ์ ํ)
response = self.model.generate(
prompt,
max_new_tokens=5000,
temperature=0.7
)
return response
def _evaluate_chain(self, chain: str, answer: str) -> float:
"""์ถ๋ก ์ฒด์ธ ํ์ง ํ๊ฐ"""
# Reward model๋ก ํ๊ฐ
score = self.reward_model.evaluate(chain)
# ์๊ธฐ ์ผ๊ด์ฑ ์ฒดํฌ
consistency_score = self._check_consistency(chain, answer)
return score * 0.7 + consistency_score * 0.3
def _check_consistency(self, chain: str, answer: str) -> float:
"""๋
ผ๋ฆฌ์ ์ผ๊ด์ฑ ๊ฒ์ฌ"""
# ๊ฐ๋จํ ํด๋ฆฌ์คํฑ ๋๋ ๋ณ๋ ๋ชจ๋ธ ์ฌ์ฉ
prompt = f"""Is this reasoning chain logically consistent?
Reasoning:
{chain}
Answer: {answer}
Rate consistency (0-1):"""
response = self.model.generate(prompt, max_new_tokens=10)
# ํ์ฑ...
return 0.8 # ์์
class TreeOfThoughts:
"""Tree of Thoughts ๊ตฌํ"""
def __init__(self, model, evaluator):
self.model = model
self.evaluator = evaluator
def solve(
self,
problem: str,
depth: int = 3,
branching_factor: int = 3
) -> str:
"""ํธ๋ฆฌ ํ์์ผ๋ก ๋ฌธ์ ํด๊ฒฐ"""
root = {"state": problem, "thoughts": [], "score": 0}
best_path = self._search(root, depth, branching_factor)
return self._extract_solution(best_path)
def _search(self, node: dict, depth: int, bf: int) -> List[dict]:
"""BFS/DFS ํ์"""
if depth == 0:
return [node]
# ๋ค์ ๋จ๊ณ ์๊ฐ๋ค ์์ฑ
thoughts = self._generate_thoughts(node, bf)
# ๊ฐ ์๊ฐ ํ๊ฐ
children = []
for thought in thoughts:
child = {
"state": node["state"],
"thoughts": node["thoughts"] + [thought],
"score": self._evaluate_thought(thought, node)
}
children.append(child)
# ์์ b๊ฐ๋ง ํ์ฅ (beam search)
children.sort(key=lambda x: x["score"], reverse=True)
children = children[:bf]
# ์ฌ๊ท ํ์
best_paths = []
for child in children:
path = self._search(child, depth - 1, bf)
best_paths.extend(path)
return sorted(best_paths, key=lambda x: x["score"], reverse=True)[:1]
def _generate_thoughts(self, node: dict, n: int) -> List[str]:
"""๋ค์ ๋จ๊ณ ์๊ฐ ์์ฑ"""
context = "\n".join(node["thoughts"])
prompt = f"""Problem: {node["state"]}
Previous thoughts:
{context}
Generate {n} different next steps or approaches:"""
response = self.model.generate(prompt)
# ํ์ฑํ์ฌ n๊ฐ ์๊ฐ ์ถ์ถ
return response.split("\n")[:n]
def _evaluate_thought(self, thought: str, node: dict) -> float:
"""์๊ฐ์ ํ์ง ํ๊ฐ"""
return self.evaluator.score(thought, node["state"])
2. Synthetic Data¶
2.1 ๊ฐ๋ ¶
Synthetic Data Generation:
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ ๋ฌธ์ : ๊ณ ํ์ง ํ์ต ๋ฐ์ดํฐ ๋ถ์กฑ โ
โ โ
โ ํด๊ฒฐ: LLM์ผ๋ก ํ์ต ๋ฐ์ดํฐ ์์ฑ โ
โ โ
โ ๋ฐฉ๋ฒ: โ
โ 1. Self-Instruct: instruction/response ์ ์์ฑ โ
โ 2. Evol-Instruct: ์ ์ง์ ๋ณต์กํ โ
โ 3. Rejection Sampling: ๋ค์ ์์ฑ ํ ํํฐ๋ง โ
โ 4. RLHF-style: ์ ํธ๋ ๋ฐ์ดํฐ ์์ฑ โ
โ 5. Distillation: ๊ฐํ ๋ชจ๋ธ์์ ์ฝํ ๋ชจ๋ธ๋ก โ
โ โ
โ ์ฃผ์: โ
โ - Model collapse (์๊ธฐ ๋ฐ์ดํฐ๋ก๋ง ํ์ต ์) โ
โ - ๋ค์์ฑ ์ ์ง ์ค์ โ
โ - ํ์ง ๊ฒ์ฆ ํ์ โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
2.2 ๊ตฌํ¶
class SyntheticDataGenerator:
"""ํฉ์ฑ ๋ฐ์ดํฐ ์์ฑ๊ธฐ"""
def __init__(self, teacher_model, student_model=None):
self.teacher = teacher_model
self.student = student_model
def generate_instruction_data(
self,
seed_instructions: List[str],
num_samples: int = 10000,
diversity_threshold: float = 0.7
) -> List[dict]:
"""Instruction-Response ๋ฐ์ดํฐ ์์ฑ"""
generated = []
instruction_embeddings = []
while len(generated) < num_samples:
# ์ instruction ์์ฑ
instruction = self._generate_instruction(seed_instructions + [
g["instruction"] for g in generated[-10:]
])
# ๋ค์์ฑ ์ฒดํฌ
if self._check_diversity(instruction, instruction_embeddings, diversity_threshold):
# Response ์์ฑ
response = self._generate_response(instruction)
# ํ์ง ์ฒดํฌ
if self._quality_check(instruction, response):
generated.append({
"instruction": instruction,
"response": response
})
# ์๋ฒ ๋ฉ ์ ์ฅ
emb = self._get_embedding(instruction)
instruction_embeddings.append(emb)
if len(generated) % 100 == 0:
print(f"Generated {len(generated)}/{num_samples}")
return generated
def _generate_instruction(self, examples: List[str]) -> str:
"""์ instruction ์์ฑ"""
examples_text = "\n".join([f"- {ex}" for ex in examples[-5:]])
prompt = f"""Here are some example instructions:
{examples_text}
Generate a new, different instruction that is:
1. Clear and specific
2. Different from the examples
3. Useful and educational
New instruction:"""
return self.teacher.generate(prompt, temperature=0.9)
def _generate_response(self, instruction: str) -> str:
"""Response ์์ฑ"""
prompt = f"""Instruction: {instruction}
Please provide a helpful, accurate, and detailed response:"""
return self.teacher.generate(prompt, temperature=0.7)
def _check_diversity(
self,
instruction: str,
existing_embeddings: List,
threshold: float
) -> bool:
"""๋ค์์ฑ ๊ฒ์ฌ"""
if not existing_embeddings:
return True
new_emb = self._get_embedding(instruction)
for emb in existing_embeddings:
similarity = self._cosine_similarity(new_emb, emb)
if similarity > threshold:
return False
return True
def _quality_check(self, instruction: str, response: str) -> bool:
"""ํ์ง ๊ฒ์ฌ"""
# ๊ธธ์ด ์ฒดํฌ
if len(response) < 50:
return False
# ๊ด๋ จ์ฑ ์ฒดํฌ (๊ฐ๋จํ ํด๋ฆฌ์คํฑ)
instruction_words = set(instruction.lower().split())
response_words = set(response.lower().split())
overlap = len(instruction_words & response_words)
if overlap < 2:
return False
return True
class RejectSampling:
"""Rejection Sampling์ผ๋ก ๊ณ ํ์ง ๋ฐ์ดํฐ ์ ๋ณ"""
def __init__(self, generator_model, reward_model):
self.generator = generator_model
self.reward = reward_model
def generate_with_rejection(
self,
prompt: str,
n_samples: int = 16,
top_k: int = 1
) -> List[str]:
"""๋ค์ ์์ฑ ํ ์ต์ ์ ํ"""
# ์ฌ๋ฌ ์๋ต ์์ฑ
responses = []
for _ in range(n_samples):
response = self.generator.generate(prompt, temperature=0.8)
responses.append(response)
# ๊ฐ ์๋ต ์ ์ํ
scored = []
for response in responses:
score = self.reward.score(prompt, response)
scored.append((response, score))
# ์์ k๊ฐ ์ ํ
scored.sort(key=lambda x: x[1], reverse=True)
return [r for r, s in scored[:top_k]]
3. Multi-Agent Systems¶
3.1 ๊ฐ๋ ¶
Multi-Agent LLM Systems:
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ Agent ์ ํ: โ
โ โ
โ 1. Debate: ์ฌ๋ฌ Agent๊ฐ ํ ๋ก โ
โ - ์๋ก ๋ค๋ฅธ ๊ด์ ์ ์ โ
โ - ํฉ์ ๋์ถ โ
โ โ
โ 2. Collaboration: ์ญํ ๋ถ๋ด ํ์
โ
โ - ์์ฑ์, ๊ฒํ ์, ํธ์ง์ โ
โ - ์ฐ๊ตฌ์, ๊ฐ๋ฐ์, ํ
์คํฐ โ
โ โ
โ 3. Competition: ๊ฒฝ์์ ์์ฑ โ
โ - ์ต์ ์ ๊ฒฐ๊ณผ ์ ํ โ
โ - Red team / Blue team โ
โ โ
โ 4. Hierarchical: ๊ณ์ธต์ ๊ตฌ์กฐ โ
โ - Manager โ Worker agents โ
โ - ํ์คํฌ ๋ถํด ๋ฐ ์์ โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
3.2 ๊ตฌํ¶
from typing import Dict, Any
from dataclasses import dataclass
from enum import Enum
class AgentRole(Enum):
PLANNER = "planner"
RESEARCHER = "researcher"
WRITER = "writer"
CRITIC = "critic"
EDITOR = "editor"
@dataclass
class Message:
sender: str
receiver: str
content: str
metadata: Dict[str, Any] = None
class MultiAgentSystem:
"""๋ค์ค ์์ด์ ํธ ์์คํ
"""
def __init__(self, llm):
self.llm = llm
self.agents = {}
self.message_history = []
def add_agent(self, name: str, role: AgentRole, system_prompt: str):
"""์์ด์ ํธ ์ถ๊ฐ"""
self.agents[name] = {
"role": role,
"system_prompt": system_prompt,
"memory": []
}
def send_message(self, sender: str, receiver: str, content: str):
"""๋ฉ์์ง ์ ์ก"""
message = Message(sender=sender, receiver=receiver, content=content)
self.message_history.append(message)
self.agents[receiver]["memory"].append(message)
return self._get_response(receiver)
def _get_response(self, agent_name: str) -> str:
"""์์ด์ ํธ ์๋ต ์์ฑ"""
agent = self.agents[agent_name]
# ์ต๊ทผ ๋ฉ์์ง๋ก ์ปจํ
์คํธ ๊ตฌ์ฑ
recent_messages = agent["memory"][-5:]
context = "\n".join([
f"{m.sender}: {m.content}" for m in recent_messages
])
prompt = f"""{agent["system_prompt"]}
Recent conversation:
{context}
Your response as {agent_name}:"""
return self.llm.generate(prompt)
def run_debate(
self,
topic: str,
agents: List[str],
rounds: int = 3
) -> str:
"""ํ ๋ก ์คํ"""
# ์ด๊ธฐ ์๊ฒฌ
opinions = {}
for agent in agents:
response = self.send_message(
"moderator", agent,
f"What is your position on: {topic}"
)
opinions[agent] = response
# ํ ๋ก ๋ผ์ด๋
for round in range(rounds):
for agent in agents:
# ๋ค๋ฅธ ์์ด์ ํธ ์๊ฒฌ ์ ๋ฌ
other_opinions = "\n".join([
f"{a}: {o}" for a, o in opinions.items() if a != agent
])
response = self.send_message(
"moderator", agent,
f"Others' opinions:\n{other_opinions}\n\nYour response:"
)
opinions[agent] = response
# ํฉ์ ๋์ถ
final_opinions = "\n".join([f"{a}: {o}" for a, o in opinions.items()])
consensus = self.llm.generate(
f"Based on this debate, summarize the consensus:\n{final_opinions}"
)
return consensus
class CollaborativeWriting:
"""ํ์
๊ธ์ฐ๊ธฐ ์์คํ
"""
def __init__(self, llm):
self.system = MultiAgentSystem(llm)
# ์์ด์ ํธ ์ค์
self.system.add_agent(
"writer",
AgentRole.WRITER,
"You are a creative writer. Write engaging content."
)
self.system.add_agent(
"critic",
AgentRole.CRITIC,
"You are a critical reviewer. Point out issues and suggest improvements."
)
self.system.add_agent(
"editor",
AgentRole.EDITOR,
"You are an editor. Refine and polish the writing."
)
def write(self, topic: str, iterations: int = 3) -> str:
"""ํ์
๊ธ์ฐ๊ธฐ"""
# ์ด์ ์์ฑ
draft = self.system.send_message(
"user", "writer",
f"Write a short article about: {topic}"
)
for i in range(iterations):
# ๋นํ
critique = self.system.send_message(
"writer", "critic",
f"Please review this draft:\n{draft}"
)
# ์์
revised = self.system.send_message(
"critic", "writer",
f"Based on this feedback:\n{critique}\n\nPlease revise the draft."
)
draft = revised
# ์ต์ข
ํธ์ง
final = self.system.send_message(
"writer", "editor",
f"Please polish this final draft:\n{draft}"
)
return final
4. World Models¶
4.1 ๊ฐ๋ ¶
World Models:
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ ๋ชฉํ: LLM์ด ์ธ๊ณ์ ๋์ ๋ฐฉ์์ ์ดํดํ๊ณ ์๋ฎฌ๋ ์ด์
โ
โ โ
โ ์์ฉ: โ
โ 1. Planning: ํ๋์ ๊ฒฐ๊ณผ ์์ธก โ
โ 2. Reasoning: ์ธ๊ณผ ๊ด๊ณ ์ถ๋ก โ
โ 3. Simulation: ๊ฐ์ ํ๊ฒฝ ์๋ฎฌ๋ ์ด์
โ
โ 4. Embodied AI: ๋ก๋ด ์ ์ด โ
โ โ
โ ์ฐ๊ตฌ ๋ฐฉํฅ: โ
โ - Video generation as world simulation (Sora) โ
โ - Physical reasoning benchmarks โ
โ - Embodied language models โ
โ - Causal reasoning โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
4.2 ๊ฐ๋ ์ ๊ตฌํ¶
class WorldModel:
"""World Model ๊ฐ๋
์ ๊ตฌํ"""
def __init__(self, llm):
self.llm = llm
self.state = {}
def initialize_state(self, description: str):
"""์ด๊ธฐ ์ํ ์ค์ """
prompt = f"""Parse this scene description into structured state.
Description: {description}
Extract:
- Objects (name, position, properties)
- Relationships between objects
- Physical constraints
State:"""
state_text = self.llm.generate(prompt)
self.state = self._parse_state(state_text)
def predict_action_result(self, action: str) -> Dict:
"""ํ๋ ๊ฒฐ๊ณผ ์์ธก"""
state_description = self._describe_state()
prompt = f"""Current state:
{state_description}
Action: {action}
Predict:
1. What changes will occur?
2. What is the new state?
3. Any unexpected effects?
Prediction:"""
prediction = self.llm.generate(prompt)
return self._parse_prediction(prediction)
def simulate_sequence(
self,
actions: List[str]
) -> List[Dict]:
"""ํ๋ ์ํ์ค ์๋ฎฌ๋ ์ด์
"""
states = [self.state.copy()]
for action in actions:
prediction = self.predict_action_result(action)
self._apply_changes(prediction)
states.append(self.state.copy())
return states
def _describe_state(self) -> str:
"""์ํ๋ฅผ ํ
์คํธ๋ก ์ค๋ช
"""
# state dict๋ฅผ ์์ฐ์ด๋ก ๋ณํ
return str(self.state)
def _parse_state(self, text: str) -> Dict:
"""ํ
์คํธ๋ฅผ ์ํ๋ก ํ์ฑ"""
# ์ค์ ๋ก๋ ๋ ์ ๊ตํ ํ์ฑ ํ์
return {"raw": text}
def _parse_prediction(self, text: str) -> Dict:
"""์์ธก ๊ฒฐ๊ณผ ํ์ฑ"""
return {"raw": text}
def _apply_changes(self, prediction: Dict):
"""์์ธก๋ ๋ณํ ์ ์ฉ"""
# ์ํ ์
๋ฐ์ดํธ
pass
5. ๋ฏธ๋ ์ฐ๊ตฌ ๋ฐฉํฅ¶
5.1 ์ฃผ์ ๋ฐฉํฅ¶
1. Scaling Laws Beyond Parameters
- Test-time compute scaling
- Mixture of Experts scaling
- Data quality over quantity
2. Multimodal Understanding
- Native multimodal models
- Embodied AI
- Physical world understanding
3. Reasoning Enhancement
- Formal verification
- Neuro-symbolic integration
- Causal reasoning
4. Alignment & Safety
- Constitutional AI
- Interpretability
- Robustness to adversarial inputs
5. Efficiency
- Sparse architectures
- Mixture of Depths
- Early exit mechanisms
5.2 ์ด๋ฆฐ ๋ฌธ์ ๋ค¶
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ ์ด๋ฆฐ ์ฐ๊ตฌ ๋ฌธ์ : โ
โ โ
โ 1. Hallucination ์์ ํด๊ฒฐ โ
โ - ์ธ์ ๋ชจ๋ฅด๋์ง ์๋ ๊ฒ โ
โ - ์ ๋ขฐ๋ calibration โ
โ โ
โ 2. True Reasoning vs Pattern Matching โ
โ - ์ง์ ํ ์ผ๋ฐํ ๋ฅ๋ ฅ? โ
โ - Out-of-distribution ์ถ๋ก โ
โ โ
โ 3. Long-term Memory โ
โ - ์๊ตฌ์ ํ์ต โ
โ - Continual learning without forgetting โ
โ โ
โ 4. Efficiency-Capability Tradeoff โ
โ - ์์ ๋ชจ๋ธ์ ํ๊ณ? โ
โ - Knowledge distillation ํ๊ณ โ
โ โ
โ 5. Alignment โ
โ - Value alignment์ ์ ์ โ
โ - Scalable oversight โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
ํต์ฌ ์ ๋ฆฌ¶
Research Frontiers ์์ฝ¶
1. o1-style: ์ถ๋ก ์ ๋ ๋ง์ ๊ณ์ฐ
2. Synthetic Data: LLM์ผ๋ก ํ์ต ๋ฐ์ดํฐ ์์ฑ
3. Multi-Agent: ํ์
/ํ ๋ก /๊ฒฝ์ ์์คํ
4. World Models: ๋ฌผ๋ฆฌ ์ธ๊ณ ์๋ฎฌ๋ ์ด์
5. Alignment: ์์ ํ๊ณ ์ ์ฉํ AI
๋ฏธ๋ ์ ๋ง¶
- Parameter scaling โ Compute scaling
- Single model โ Multi-agent systems
- Text โ Native multimodal
- Pattern matching โ True reasoning
- Black box โ Interpretable
์ฐธ๊ณ ์๋ฃ¶
- OpenAI (2024). "Learning to Reason with LLMs" (o1)
- Yao et al. (2023). "Tree of Thoughts: Deliberate Problem Solving with Large Language Models"
- Park et al. (2023). "Generative Agents: Interactive Simulacra of Human Behavior"
- Ha & Schmidhuber (2018). "World Models"
- Sora Technical Report (2024)