RLHF

PPO in LLMs vs PPO in Walker2D

🚀 Why RLHF? #

Reinforcement Learning from Human Feedback (RLHF) is a foundational technique for aligning large language models (LLMs) with human preferences. Rather than optimizing for likelihood of next-token prediction alone, RLHF adds a human-in-the-loop feedback mechanism to fine-tune model behavior.

Modern AI systems — especially LLMs — are capable of generating coherent text, code, and dialogue. However, raw model outputs often:

  • Lack safety or factuality
  • Misalign with user intent
  • Fail to follow task constraints

RLHF helps solve this by using human preferences to shape model behavior in alignment with real-world goals.

🧠 Core Components #

RLHF typically unfolds in three stages:

  1. Supervised Fine-Tuning (SFT)
    • A pretrained LLM is fine-tuned on curated high-quality prompts and responses.
  2. Reward Modeling
    • A separate model is trained to predict human preference scores (better vs worse answers).
  3. Reinforcement Learning (PPO)
    • The main model is optimized using a reward signal from the reward model, via Proximal Policy Optimization.

Each of these stages ensures the model is not just fluent — but aligned.

🧩 Topics Covered Here #

  • ✍️ Human Preference Collection
    • Paired responses, rating scales, feedback annotation
  • 🧱 Reward Modeling
    • Architecture, loss functions, dataset design
  • ♻️ Reinforcement Learning Fine-Tuning
    • PPO algorithm, TRL (HuggingFace), hyperparameter tuning
  • 🧪 Evaluating RLHF Models
    • Alignment metrics, human evals, reward hacking prevention
  • 🔁 Comparison with Other Alignment Methods
    • DPO, Constitutional AI, Self-Instruct

🧭 Why RLHF Belongs in Alignment-Reasoning #

RLHF is not just a training trick — it’s a method for structuring model behavior using human feedback as the reward signal. It represents a bridge between:

  • Optimization (via reinforcement learning)
  • Intent modeling (via human preference)
  • Structural alignment (via value feedback loops)

That’s why RLHF sits squarely within the broader alignment-reasoning framework of this knowledge base.