RLHF and Alignment Techniques AI Prompt

Design an alignment fine-tuning pipeline to improve helpfulness, harmlessness, and honesty.

Base model: {{base_model}} (already instruction-tuned or raw)
Alignment goal: {{goal}} (reduce refusals, improve helpfulness, enforce tone, reduce hallucination)
Resources: {{resources}} (GPU count, annotation budget)

1. The alignment pipeline overview:

   Stage 1 — Supervised Fine-Tuning (SFT):
   - Fine-tune on high-quality human demonstrations of the desired behavior
   - Creates the 'SFT model' — a good baseline for the target behavior

   Stage 2 — Reward Model Training:
   - Collect human preference data: show pairs of responses to the same prompt, ask which is better
   - Train a reward model to predict human preferences
   - The RM maps (prompt, response) → a scalar reward score

   Stage 3 — RLHF (PPO or similar):
   - Use the reward model to optimize the SFT model via reinforcement learning
   - PPO (Proximal Policy Optimization): standard RL algorithm for LLM fine-tuning
   - KL penalty: prevent the model from deviating too far from the SFT model (avoids reward hacking)

2. DPO (Direct Preference Optimization) — simplified alternative to RLHF:
   - Requires: preference dataset of (prompt, chosen_response, rejected_response) pairs
   - Directly optimizes the policy using a classification-style loss — no separate reward model needed
   - Much simpler to implement than PPO-based RLHF
   - Quality: competitive with PPO for most alignment tasks
   - Loss: L_DPO = -log sigmoid(beta * (log π(chosen|x) / π_ref(chosen|x) - log π(rejected|x) / π_ref(rejected|x)))
   - beta: temperature controlling strength of preference learning (default 0.1-0.5)

3. Preference data collection:
   - Red teaming prompts: adversarial inputs designed to elicit unwanted behavior
   - Helpful task prompts: standard task inputs where response quality varies
   - For each prompt: collect 2-4 model responses, have annotators rank or choose the best
   - Annotator guidelines: define precisely what 'better' means (more helpful? less harmful? more accurate?)

4. ORPO (Odds Ratio Preference Optimization):
   - Combines SFT and preference optimization in a single training stage
   - Simpler than the SFT → DPO two-stage pipeline
   - Good default for limited compute budgets

5. Constitutional AI (CAI) approach:
   - Specify a set of principles ('constitution') that the model should follow
   - Use the model itself to critique and revise its own responses against the constitution
   - Reduces dependence on human preference annotation

Return: alignment pipeline selection (full RLHF vs DPO vs ORPO), preference data collection plan, training configuration, and evaluation approach.