🔍 Overview

RL-Chat is a conversational AI system that integrates reinforcement learning principles to improve large language model (LLM) responses based on feedback signals.

The project explores how conversational quality can be enhanced through reward modelling, feedback loops and iterative policy optimisation. It demonstrates applied reinforcement learning concepts within a practical chatbot framework.

🎯 Objectives

• Build an interactive LLM-based chat system

• Integrate feedback-driven optimisation

• Simulate reinforcement learning from feedback

• Improve response relevance and coherence over time

• Demonstrate RLHF-style architecture principles

🏗️ System Architecture User Input -> Base LLM Response -> Feedback Signal (Reward) -> Policy Update / Optimisation -> Improved Response Generation

🧠 Core Concepts

• Reinforcement Learning (RL)

• Reward Modelling

• Policy Optimisation

• Feedback Loops

• Human-in-the-Loop Learning

• LLM Fine-Tuning Simulation

⚙️ Implementation Highlights

• Chat interface for real-time interaction

• Feedback collection mechanism

• Reward signal integration

• Iterative response refinement

• Modular training pipeline

🛠️ Tech Stack

• Python

• PyTorch / TensorFlow

• NumPy

• Reinforcement Learning utilities