25. Research Frontiers
25. Research Frontiers¶
Overview¶
This lesson explores the cutting edge of Foundation Model research. We investigate future directions including World Models, o1-style Reasoning, Synthetic Data, and Multi-Agent systems.
1. o1-style Reasoning (Test-time Compute)¶
1.1 Concept¶
Traditional LLM vs o1-style:
┌─────────────────────────────────────────────────────────┐
│ Traditional LLM: │
│ - Focus computation at training time │
│ (larger models, more data) │
│ - Fixed forward pass during inference │
│ - Limitations on complex problems │
│ │
│ o1-style (Test-time Compute Scaling): │
│ - Use more computation during inference │
│ - Automatic Chain-of-Thought generation │
│ - Explore multiple paths, select best │
│ - Adaptive computation based on problem difficulty │
└─────────────────────────────────────────────────────────┘
Key Techniques:
1. Internal Chain-of-Thought
2. Search/Verification loops
3. Self-consistency checking
4. Reward model guided search
1.2 Conceptual Implementation¶
import torch
from typing import List, Tuple
class ReasoningModel:
"""o1-style reasoning model (conceptual implementation)"""
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:
"""Extended reasoning"""
# 1. Generate multiple reasoning chains
candidates = self._generate_candidates(problem, num_candidates)
# 2. Evaluate each chain
scored_candidates = []
for chain, answer in candidates:
score = self._evaluate_chain(chain, answer)
scored_candidates.append((chain, answer, score))
# 3. Select best answer
best = max(scored_candidates, key=lambda x: x[2])
return best[1] # Return only answer (chain is internal)
def _generate_candidates(
self,
problem: str,
n: int
) -> List[Tuple[str, str]]:
"""Generate multiple reasoning paths"""
candidates = []
for _ in range(n):
# Generate step-by-step reasoning
chain = self._generate_reasoning_chain(problem)
# Extract final answer from chain
answer = self._extract_answer(chain)
candidates.append((chain, answer))
return candidates
def _generate_reasoning_chain(self, problem: str) -> str:
"""Generate reasoning chain"""
prompt = f"""Solve this problem step by step.
Think carefully and show your reasoning.
Problem: {problem}
Let me think through this carefully..."""
# Generate without length limit (or very long limit)
response = self.model.generate(
prompt,
max_new_tokens=5000,
temperature=0.7
)
return response
def _evaluate_chain(self, chain: str, answer: str) -> float:
"""Evaluate reasoning chain quality"""
# Evaluate with reward model
score = self.reward_model.evaluate(chain)
# Self-consistency check
consistency_score = self._check_consistency(chain, answer)
return score * 0.7 + consistency_score * 0.3
def _check_consistency(self, chain: str, answer: str) -> float:
"""Check logical consistency"""
# Simple heuristic or separate model
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)
# Parse...
return 0.8 # Example
class TreeOfThoughts:
"""Tree of Thoughts implementation"""
def __init__(self, model, evaluator):
self.model = model
self.evaluator = evaluator
def solve(
self,
problem: str,
depth: int = 3,
branching_factor: int = 3
) -> str:
"""Solve with tree search"""
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 search"""
if depth == 0:
return [node]
# Generate next step thoughts
thoughts = self._generate_thoughts(node, bf)
# Evaluate each thought
children = []
for thought in thoughts:
child = {
"state": node["state"],
"thoughts": node["thoughts"] + [thought],
"score": self._evaluate_thought(thought, node)
}
children.append(child)
# Expand only top b (beam search)
children.sort(key=lambda x: x["score"], reverse=True)
children = children[:bf]
# Recursive search
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]:
"""Generate next step thoughts"""
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)
# Parse to extract n thoughts
return response.split("\n")[:n]
def _evaluate_thought(self, thought: str, node: dict) -> float:
"""Evaluate thought quality"""
return self.evaluator.score(thought, node["state"])
2. Synthetic Data¶
2.1 Concept¶
Synthetic Data Generation:
┌─────────────────────────────────────────────────────────┐
│ Problem: Shortage of high-quality training data │
│ │
│ Solution: Generate training data with LLMs │
│ │
│ Methods: │
│ 1. Self-Instruct: Generate instruction/response pairs │
│ 2. Evol-Instruct: Progressive complexity increase │
│ 3. Rejection Sampling: Generate many, filter best │
│ 4. RLHF-style: Generate preference data │
│ 5. Distillation: Strong model to weak model │
│ │
│ Cautions: │
│ - Model collapse (training only on own data) │
│ - Maintain diversity │
│ - Quality verification essential │
└─────────────────────────────────────────────────────────┘
2.2 Implementation¶
class SyntheticDataGenerator:
"""Synthetic data generator"""
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]:
"""Generate Instruction-Response data"""
generated = []
instruction_embeddings = []
while len(generated) < num_samples:
# Generate new instruction
instruction = self._generate_instruction(seed_instructions + [
g["instruction"] for g in generated[-10:]
])
# Check diversity
if self._check_diversity(instruction, instruction_embeddings, diversity_threshold):
# Generate response
response = self._generate_response(instruction)
# Quality check
if self._quality_check(instruction, response):
generated.append({
"instruction": instruction,
"response": response
})
# Save embedding
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:
"""Generate new instruction"""
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:
"""Generate response"""
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:
"""Diversity check"""
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:
"""Quality check"""
# Length check
if len(response) < 50:
return False
# Relevance check (simple heuristic)
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:
"""Select high-quality data with Rejection Sampling"""
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]:
"""Generate many, select best"""
# Generate multiple responses
responses = []
for _ in range(n_samples):
response = self.generator.generate(prompt, temperature=0.8)
responses.append(response)
# Score each response
scored = []
for response in responses:
score = self.reward.score(prompt, response)
scored.append((response, score))
# Select top k
scored.sort(key=lambda x: x[1], reverse=True)
return [r for r, s in scored[:top_k]]
3. Multi-Agent Systems¶
3.1 Concept¶
Multi-Agent LLM Systems:
┌─────────────────────────────────────────────────────────┐
│ Agent Types: │
│ │
│ 1. Debate: Multiple agents discuss │
│ - Present different perspectives │
│ - Reach consensus │
│ │
│ 2. Collaboration: Role-based cooperation │
│ - Writer, Reviewer, Editor │
│ - Researcher, Developer, Tester │
│ │
│ 3. Competition: Competitive generation │
│ - Select best result │
│ - Red team / Blue team │
│ │
│ 4. Hierarchical: Hierarchical structure │
│ - Manager → Worker agents │
│ - Task decomposition and delegation │
└─────────────────────────────────────────────────────────┘
3.2 Implementation¶
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:
"""Multi-agent system"""
def __init__(self, llm):
self.llm = llm
self.agents = {}
self.message_history = []
def add_agent(self, name: str, role: AgentRole, system_prompt: str):
"""Add agent"""
self.agents[name] = {
"role": role,
"system_prompt": system_prompt,
"memory": []
}
def send_message(self, sender: str, receiver: str, content: str):
"""Send message"""
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:
"""Generate agent response"""
agent = self.agents[agent_name]
# Compose context from recent messages
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:
"""Run debate"""
# Initial opinions
opinions = {}
for agent in agents:
response = self.send_message(
"moderator", agent,
f"What is your position on: {topic}"
)
opinions[agent] = response
# Debate rounds
for round in range(rounds):
for agent in agents:
# Share other agents' opinions
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
# Reach consensus
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:
"""Collaborative writing system"""
def __init__(self, llm):
self.system = MultiAgentSystem(llm)
# Setup agents
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:
"""Collaborative writing"""
# Draft
draft = self.system.send_message(
"user", "writer",
f"Write a short article about: {topic}"
)
for i in range(iterations):
# Critique
critique = self.system.send_message(
"writer", "critic",
f"Please review this draft:\n{draft}"
)
# Revise
revised = self.system.send_message(
"critic", "writer",
f"Based on this feedback:\n{critique}\n\nPlease revise the draft."
)
draft = revised
# Final editing
final = self.system.send_message(
"writer", "editor",
f"Please polish this final draft:\n{draft}"
)
return final
4. World Models¶
4.1 Concept¶
World Models:
┌─────────────────────────────────────────────────────────┐
│ Goal: LLMs understand and simulate how the world works │
│ │
│ Applications: │
│ 1. Planning: Predict consequences of actions │
│ 2. Reasoning: Causal relationship inference │
│ 3. Simulation: Virtual environment simulation │
│ 4. Embodied AI: Robot control │
│ │
│ Research Directions: │
│ - Video generation as world simulation (Sora) │
│ - Physical reasoning benchmarks │
│ - Embodied language models │
│ - Causal reasoning │
└─────────────────────────────────────────────────────────┘
4.2 Conceptual Implementation¶
class WorldModel:
"""World Model conceptual implementation"""
def __init__(self, llm):
self.llm = llm
self.state = {}
def initialize_state(self, description: str):
"""Set initial state"""
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:
"""Predict action result"""
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]:
"""Simulate action sequence"""
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:
"""Describe state as text"""
# Convert state dict to natural language
return str(self.state)
def _parse_state(self, text: str) -> Dict:
"""Parse text to state"""
# More sophisticated parsing needed in practice
return {"raw": text}
def _parse_prediction(self, text: str) -> Dict:
"""Parse prediction result"""
return {"raw": text}
def _apply_changes(self, prediction: Dict):
"""Apply predicted changes"""
# Update state
pass
5. Future Research Directions¶
5.1 Key Directions¶
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 Open Problems¶
┌─────────────────────────────────────────────────────────┐
│ Open Research Problems: │
│ │
│ 1. Complete Hallucination Resolution │
│ - Knowing when you don't know │
│ - Confidence calibration │
│ │
│ 2. True Reasoning vs Pattern Matching │
│ - True generalization ability? │
│ - Out-of-distribution reasoning │
│ │
│ 3. Long-term Memory │
│ - Permanent learning │
│ - Continual learning without forgetting │
│ │
│ 4. Efficiency-Capability Tradeoff │
│ - Limitations of small models? │
│ - Knowledge distillation limits │
│ │
│ 5. Alignment │
│ - Definition of value alignment │
│ - Scalable oversight │
└─────────────────────────────────────────────────────────┘
Key Summary¶
Research Frontiers Summary¶
1. o1-style: More computation at inference time
2. Synthetic Data: Generate training data with LLMs
3. Multi-Agent: Collaboration/debate/competition systems
4. World Models: Physical world simulation
5. Alignment: Safe and useful AI
Future Outlook¶
- Parameter scaling → Compute scaling
- Single model → Multi-agent systems
- Text → Native multimodal
- Pattern matching → True reasoning
- Black box → Interpretable
References¶
- 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)