10. RLHF (Reinforcement Learning from Human Feedback) Architecture: Reward Models & PPO (Proximal Policy Optimization) • Model Fine-Tuning, Quantization & RLHF (PEFT, LoRA, vLLM) • Rishabh Ki Kaksha

Model Fine-Tuning, Quantization & RLHF (PEFT, LoRA, vLLM) › 10. RLHF (Reinforcement Learning from Human Feedback) Architecture: Reward Models & PPO (Proximal Policy Optimization)

50 mins

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Course Syllabus

Module 1: Pre-training vs Fine-Tuning & Dataset Engineering

01. LLM Adaptation Paradigms: Pretraining, Continued Pretraining, Full Fine-Tuning vs PEFT 02. Fine-Tuning Dataset Engineering: Instruction Datasets (Alpaca/ShareGPT formats), Quality Filtering & Deduplication 03. Tokenization Traps: Prompt Formatting, Chat Templates (ChatML / Llama-3 format) & Loss Masking on Prompts

Module 2: Parameter-Efficient Fine-Tuning (PEFT) & LoRA / QLoRA Internals

04. Low-Rank Adaptation (LoRA) Mathematics: Intrinsic Rank Hypothesis, Matrix Factorization (W + B*A) & Alpha Scaling 05. QLoRA Deep Dive: 4-Bit NormalFloat (NF4), Double Quantization & Paged Optimizers for VRAM Efficiency 06. Target Modules Selection (q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj) & Rank Tradeoffs

Module 3: Supervised Fine-Tuning (SFT) with HuggingFace TRL & Unsloth

07. Hugging Face TRL (Transformer Reinforcement Learning) & SFTTrainer Architecture 08. Ultra-Fast Fine-Tuning with Unsloth (Manual Triton Kernels & 5x Speedup on Single GPUs) 09. Monitoring Loss Curves: Overfitting Detection, Validation Perplexity & Catastrophic Forgetting Mitigation

Module 4: Alignment Algorithms: DPO (Direct Preference Optimization) & RLHF

10. RLHF (Reinforcement Learning from Human Feedback) Architecture: Reward Models & PPO (Proximal Policy Optimization) 11. Direct Preference Optimization (DPO): Eliminating Reward Models via Implicit Policy Optimization Math 12. ORPO (Odds Ratio Preference Optimization) & KTO (Kahneman-Tversky Optimization) Alignment Techniques

Module 5: Model Quantization (GGUF, AWQ, GPTQ) & High-Throughput vLLM Serving

13. Post-Training Quantization (PTQ): 4-Bit & 8-Bit Quantization Mathematics (Zero Point & Scale Factors) 14. Quantization Formats: GGUF (llama.cpp on CPU/Mac), AWQ (Activation-aware Weight Quantization) & GPTQ 15. Merging LoRA Adapters back into Base Model Weights for Zero-Inference Latency Overhead 16. High-Throughput Enterprise Model Serving with vLLM (PagedAttention, Continuous Batching & Tensor Parallelism)

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# 10. RLHF (Reinforcement Learning from Human Feedback) Architecture: Reward Models & PPO (Proximal Policy Optimization)

> Model Fine-Tuning & Quantization Notes by Rishabh - Topic 10

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### 1. The 3-Step RLHF Pipeline (InstructGPT / ChatGPT Architecture 🤖)

```
[ Step 1: Supervised Fine-Tuning (SFT) ] ➔ Pre-trained base model aligned to instructions
                        ⬇
[ Step 2: Reward Model Training (RM) ]  ➔ Pairs of (Chosen vs Rejected) responses ➔ Outputs scalar Score
                        ⬇
[ Step 3: PPO Reinforcement Learning ]  ➔ Policy Model optimizes generation to maximize Reward Score!
```

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### 2. The PPO Objective with KL Penalty ⚖️

$$\max_\theta \mathbb{E}\left[ R(x, y) - \beta \mathbb{D}_{\text{KL}}\left( \pi_\theta(y \mid x) \,\|\, \pi_{\text{ref}}(y \mid x) \right) \right]$$

*   **$R(x, y)$:** Reward Model se mila hua score.
*   **$\beta \mathbb{D}_{\text{KL}}$ (Kullback-Leibler Divergence):** Policy model $\pi_\theta$ ko original reference model $\pi_{\text{ref}}$ se zyada door deviate (Reward Hacking / Gibberish output) hone par penalize karta hai!

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### 3. Interview Ke 3 Sawal Is Topic Se

1. **Reward Hacking kya hota hai?** -> Model Reward model ke loopholes exploit karke senseless repetitive words output karke artificially high reward score generate karne lagta hai.
2. **Bradley-Terry Preference Model formula kya hota hai?** -> $P(y_w \succ y_l \mid x) = \sigma(r(x, y_w) - r(x, y_l))$ (Chosen response $y_w$ ka score rejected response $y_l$ se zyada hona).
3. **PPO RLHF setup me simultaneously kitne models VRAM me load hote hain?** -> 4 Models! (1. Actor Policy $\pi_\theta$, 2. Reference Model $\pi_{\text{ref}}$, 3. Reward Model $R_\psi$, 4. Critic Value Model $V_\phi$) - Massive GPU requirement!

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**Next Topic:** 11. Direct Preference Optimization (DPO): Eliminating Reward Models via Implicit Policy Optimization Math

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