The Complexity of RLHF
RLHF requires maintaining three separate models during training: the base model, a reward model, and a reference model. This requires immense VRAM and is notoriously unstable to train due to the PPO algorithm.
Why DPO Wins
DPO reformulates the problem to directly optimize the policy using cross-entropy loss on preference pairs (chosen vs rejected). It eliminates the need for a separate reward model, cutting memory requirements and dramatically increasing training stability.
Internal Resources
- Inference Cost Calculator
- Latency Estimator
- Tabular vs Deep Learning
- Local LLMs vs Managed APIs
- Time Series Baselines
- Tabular Model Selector
- Vision Architecture Selector
- VRAM Calculator
- Choosing Embeddings
- CNN vs ViT in 2024
- RLHF vs DPO
- RAG Chunking Strategies
- RAG Chunk Size Calculator
- Token Ratio Estimator
- Synthetic Data Generation
- Quantization Methods Explained
- Quantization Memory Savings
- Fine-Tuning vs LoRA
- LoRA Rank Calculator
- Multimodal Model Landscape