LLM Alignment via Reinforcement Learning from Human Feedback (RLHF)

Overview

This project demonstrates how to align Large Language Models (LLMs) using Reinforcement Learning from Human Feedback (RLHF). It includes training scripts for fine-tuning GPT-2 on the IMDB dataset to generate more positive movie reviews.

Installation

To set up the project environment, follow these steps:

1. Install Python dependencies: pip install -r requirements.txt

2. Run the main script: python main.py

Structure

• data/: Contains the dataset loading and preprocessing logic.
• models/: Contains the model definitions and training logic.
• utils/: Contains configuration settings and other utilities.
• main.py: The entry point of the project.

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Explanation of the LLM Alignment Algorithm Used in the PPOTrainer

The alignment of Large Language Models (LLMs) using Reinforcement Learning from Human Feedback (RLHF) is based on the idea of improving or directing the behavior of a model based on specific human-defined criteria or goals. This project focuses on fine-tuning a generative model, specifically GPT-2, to produce more positive movie reviews by employing an RLHF strategy known as Proximal Policy Optimization (PPO). Here's a deeper look into the components and operations of this alignment algorithm:

1. Proximal Policy Optimization (PPO) Overview

PPO is a policy gradient method for reinforcement learning, which balances the twin goals of allowing some amount of policy change while preventing destabilizing large updates. This approach is characterized by the following key components:

• Clipped probability ratios: This ensures that the updates to the policy do not deviate too much from the current policy, which helps in maintaining the training stability.
• Objective function: The PPO objective function uses the clipped probability ratios, creating a lower bound on the policy update. This "clipping" mechanism is where the name "Proximal" comes from, indicating that the new policy should not be too far from the old policy.
• Advantage estimation: It estimates how much better or worse it is to take a particular action compared to the average. This estimation helps in optimizing the policy towards more rewarding actions.

2. Sentiment Analysis as a Reward Function

To align the GPT-2 model towards generating positive sentiments, the algorithm utilizes a sentiment analysis model, which serves as the reward function. Here's how this works:

• Sentiment scoring: After generating responses, each response is passed through a sentiment analysis model that predicts how positive or negative the response is.
• Reward calculation: The sentiment scores are converted into rewards, with higher scores for more positive responses. This feedback loop encourages the LLM to "learn" to produce more positive outputs, as it directly links the model's outputs to desirable outcomes (positive sentiments).

3. Integration in the Training Loop

The integration of PPO with sentiment analysis in the training loop involves the following steps:

• Response generation: At each step, the model generates text responses based on input prompts from the dataset.
• Evaluation and rewards: Each response is evaluated for positivity using the sentiment model, and rewards are assigned based on these evaluations.
• Policy update: The PPO algorithm updates the model's policy (how it generates text) based on the rewards, using the gradient ascent method to maximize the expected reward.

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