Understanding Reinforcement Learning, RLHF, PPO and GRPO for AI Applications

Understanding Reinforcement Learning

DeepSeek‑R1‑Zero relies on the GRPO reinforcement‑learning framework, employing a "group‑relative policy optimization" approach that improves inference strategies without requiring explicit labeled data by comparing and optimizing within a group.

The model then filters and fine‑tunes its generated results to better match human expectations and preferences. Unlike traditional training, R1 discards the fine‑tuning stage and directly enhances inference ability through reinforcement learning, making it an effective training route when a solid pre‑trained model is available.

A key question arises for AI‑driven applications such as AI doctors or AI lawyers: do developers need to understand reinforcement learning?

Why Understanding Reinforcement Learning Is Necessary

Every industry has its own jargon—e.g., medical terms like "inflammation" and legal terms like "case"—which can cause models to misinterpret domain‑specific language, leading to hallucinations. Private knowledge bases exacerbate this problem because external observers cannot decipher internal terminology.

Two technical paths are proposed to address industry‑specific hallucinations and private‑knowledge‑base recognition issues:

Model API + Knowledge Base : Works well in practice but raises data‑leak concerns, especially in sensitive fields such as healthcare, law, and finance.

Private Model Training : Historically considered costly due to the expense of pre‑training data, high‑quality fine‑tuning data, and human‑feedback data, as well as GPU costs.

With the emergence of DeepSeek, training costs have dropped to affordable levels, and cloud tools further lower the entry barrier, making model training attractive. The core advantage is the ability to deploy models privately, solving data‑leak issues while only incurring costs for re‑training when swapping models.

Reinforcement Learning from Human Feedback (RLHF)

RLHF (Reinforcement Learning with Human Feedback) lets a model improve its performance through human guidance. The process involves four modules:

1. Actor Model (the AI doctor)

The actor generates a diagnosis based on patient symptoms, e.g., "flu" for headache and fever.

2. Reference Model

A copy of the actor that provides a standard answer for comparison, acting as a benchmark.

3. Reward Model

Evaluates the actor’s output using human feedback, assigning a high score for correct diagnoses and a low score for errors. The reward is a numeric quality score, not a next‑token probability.

4. Critic Model

A replica of the reward model that assesses the reward model’s performance, helping the actor refine its learning strategy.

In practice, the actor outputs a probability distribution over possible next tokens. RLHF adjusts this distribution by computing gradients (policy‑gradient methods such as PPO or GRPO) and updating model parameters so that high‑reward outputs become more probable.

Policy‑Gradient Methods: PPO and GRPO

PPO (Proximal Policy Optimization) is a widely used RL algorithm that clips policy updates to keep changes within a small range, ensuring stable training. It is suitable for simple tasks with limited action spaces, low computational resources, and short training cycles—e.g., robot control, game AI, and intelligent customer service.

GRPO (Group‑Relative Policy Optimization) handles multiple strategies simultaneously, making it ideal for complex, multi‑agent scenarios such as collaborative robotics, industrial automation, and high‑stability medical decision systems. GRPO’s group‑relative mechanism balances updates across strategies, preventing any single policy from destabilizing the system.

In summary, PPO excels in simple, resource‑constrained environments, while GRPO shines in complex, multi‑strategy tasks requiring high stability.

Conclusion

Reinforcement learning is opening new pathways for optimizing AI models. Whether using the simple yet efficient PPO for straightforward tasks or the robust GRPO for intricate multi‑agent applications, RL techniques are crucial for advancing AI doctors, AI lawyers, and other domain‑specific agents while addressing data security and privacy challenges.