Alignment Data

Aligning Models with Human Values via RLHF

Reinforcement Learning from Human Feedback (RLHF) represents an advanced fine-tuning approach that shapes model behavior according to human preferences. This process typically follows initial supervised training and aims to align model outputs with human values, safety requirements, and desired behaviors.

RLHF has been instrumental in creating helpful, harmless, and honest AI assistants by incorporating human judgment directly into the training process.

The RLHF Workflow

The RLHF process comprises several key stages:

Preference Collection

Models generate multiple responses per prompt, which humans then rank or rate based on quality criteria.

Preferences often use 1-7 Likert scales, potentially broken down by attributes like accuracy, usefulness, and tone.

Reward Model Training

A separate model learns to predict human preferences based on the collected ranking data.

Policy Optimization

The main model is updated using reinforcement learning algorithms to maximize the predicted reward.

Preference Data Collection

Response Generation and Ranking

For each prompt, the model generates multiple candidate responses that annotators evaluate:

{
  "prompt": "Explain quantum computing to a beginner",
  "responses": [
    {
      "text": "Quantum computing uses quantum bits...",
      "rank": 1
    },
    {
      "text": "Think of it like a super powerful computer...",
      "rank": 2
    },
    {
      "text": "Quantum computers are machines that...",
      "rank": 3
    }
  ]
}

Evaluation Criteria

Human annotators typically assess responses across multiple dimensions:

Factual correctness
Logical consistency
Completeness of information
Absence of hallucinations

Model Optimization Approaches

Traditional RLHF Pipeline

Reward Model Training

The reward model learns to predict human preferences:

# Conceptual reward model training
def train_reward_model(preference_data):
    for prompt, response_a, response_b, preference in preference_data:
        score_a = reward_model(prompt, response_a)
        score_b = reward_model(prompt, response_b)
        loss = compute_ranking_loss(score_a, score_b, preference)
        optimize(loss)

Key considerations:

Requires substantial preference data (typically 50K+ comparisons)
Model architecture often mirrors the base model
Calibration is crucial for accurate reward prediction

PPO (Proximal Policy Optimization)

PPO updates the main model using the reward signal:

# Simplified PPO update
def ppo_update(model, reward_model, prompts):
    for prompt in prompts:
        response = model.generate(prompt)
        reward = reward_model(prompt, response)
        advantage = compute_advantage(reward)
        update_policy(model, advantage, kl_constraint)

Challenges:

Computationally intensive
Requires careful hyperparameter tuning
Risk of reward hacking

Resource Requirements and Considerations

When to Use RLHF

Ideal Use Cases

General-purpose assistants
Safety-critical applications
Complex subjective tasks
High-stakes decision support

Consider Alternatives

Narrow domain applications
Objective task optimization
Limited annotation budget
Rapid prototyping needs

Best Practices for Preference Data

Annotator Training

Develop comprehensive guidelines and training programs:

Clear rating criteria
Example comparisons
Edge case handling
Consistency checks

Quality Control

Implement robust quality assurance:

Inter-rater reliability metrics
Golden standard examples
Regular calibration sessions
Outlier detection

Diversity Considerations

Ensure representative feedback:

Diverse annotator backgrounds
Multiple geographic regions
Various use case scenarios
Different user personas

Iterative Refinement

Continuously improve the process:

Regular guideline updates
Feedback incorporation
Performance monitoring
A/B testing of criteria

Continuous Learning from Deployment

Post-deployment user interactions provide valuable preference data for ongoing alignment improvements.

Work with MangoDesk

Types of data

Aligning Models with Human Values via RLHF

The RLHF Workflow

Preference Data Collection

Response Generation and Ranking

Evaluation Criteria

Model Optimization Approaches

Traditional RLHF Pipeline

Resource Requirements and Considerations

When to Use RLHF

Ideal Use Cases

Consider Alternatives

Best Practices for Preference Data

Continuous Learning from Deployment

Feedback Collection Strategies

Explicit Feedback

Implicit Signals

Work with MangoDesk

Types of data

​Aligning Models with Human Values via RLHF

​The RLHF Workflow

​Preference Data Collection

​Response Generation and Ranking

​Evaluation Criteria

​Model Optimization Approaches

​Traditional RLHF Pipeline

​Resource Requirements and Considerations

​When to Use RLHF

Ideal Use Cases

Consider Alternatives

​Best Practices for Preference Data

​Continuous Learning from Deployment

​Feedback Collection Strategies

Explicit Feedback

Implicit Signals

Aligning Models with Human Values via RLHF

The RLHF Workflow

Preference Data Collection

Response Generation and Ranking

Evaluation Criteria

Model Optimization Approaches

Traditional RLHF Pipeline

Resource Requirements and Considerations

When to Use RLHF

Best Practices for Preference Data

Continuous Learning from Deployment

Feedback Collection Strategies