AI Research
MCTS Conversation Simulator for Enhanced EQ
MCTS-based conversation simulator generating synthetic datasets for PPO reinforcement learning and RLHF training.
Project Overview
Developed a Monte Carlo Tree Search (MCTS) based conversation simulator that generates high-quality synthetic datasets for training emotional intelligence in large language models.
The system uses self-play and tree search algorithms to create diverse conversational scenarios, enabling companies to train more empathetic AI systems without relying on human-generated data.
Exposed as an MCP (Model Context Protocol) server, allowing seamless integration with existing LLM training pipelines and enabling scalable synthetic data generation.
Key Features
- MCTS-based conversation tree exploration for diverse dialogue generation
- MCP server implementation for easy enterprise integration
- 84% improvement in empathetic response generation compared to baseline models
- Self-play mechanisms for autonomous dataset expansion
- Configurable emotional intelligence metrics and evaluation frameworks
Technologies Used
PythonMCTSPPORLHFMCPPyTorchTransformers
Project Details
Client
Personal Project
Timeline
2025
Role
Lead Research Engineer
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