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20. Online Fine-Tuning and RLHF Pipeline

Authors
Affiliations
Birmingham City University
Sunway College Kathmandu

Objective

Enables continuous model improvement through reinforcement learning from human feedback (RLHF), training reward models and updating base models online.

System Architecture

[Mermaid diagram - flowchart showing core components and data flow]

[3-5 sentence description of architecture]

Technical Approach

Key Components

Pipeline / Data Flow

[Detailed description of request → processing → response flow]

Complexity Analysis

MetricComplexityNotes
Model sizeBase: 7B-70B, Reward: 1B-7B[implications]
Time complexityO(iterations × batch_size × seq_len²)[notes]
Space complexity~4-6x base model size[notes]
Latency targetN/A (batch training)[real-time vs. batch]
Throughput target100-1000 training examples/day[per GPU/instance]

Pros & Cons

Pros

Cons

Trade-offs

[1-2 paragraphs discussing key technical trade-offs]

Real-World Applications

Where This Pattern Appears

Production Considerations

[2-3 paragraphs on scaling, failure modes, monitoring, cost]

References & Citations

Citation 1: Architecture & Design

Title: [Paper/Blog Title on Online Fine-Tuning and RLHF Pipeline Architecture]

Citation 2: Performance & Benchmarks

Title: [Performance Benchmarks for Online Fine-Tuning and RLHF Pipeline]

Citation 3: Implementation Details

Title: [Implementation Details and Trade-offs]

Citation 4: Real-World Deployment

Title: [Production Deployment Insights]

Reproducibility Checklist

Infrastructure
LLM Serving Infrastructure
Infrastructure
ML Feature Store for GenAI