•Alignment means teaching a pre-trained language model to act the way people want: safe, helpful, and harmless. A pre-trained model is like a bag of knowledge with no idea how to use it, so it may hallucinate or say unsafe things. Alignment adds an outer layer of behavior so the model answers clearly, avoids harm, and respects user intent.
•Supervised Fine-Tuning (SFT) is the first common step. Humans write example prompts and high-quality answers, and the model learns to imitate those answers. This makes the model follow instructions better, respond in the right format, and be more polite and accurate.
•Collecting SFT data can be done by hiring annotators, reusing public datasets, or mixing both. Clear instructions for annotators, examples of good and bad responses, and multiple reviewers per prompt improve quality. The training objective is standard: maximize the probability of the human demonstration given the prompt.
•Choosing tasks for SFT depends on the final product goals: Q&A, instruction following, dialogue, summarization, or code. Larger datasets and models help, but hyperparameters like learning rate, batch size, and regularization matter too. SFT can be expensive and brittle across domains, yet it is widely used and effective.
•Reinforcement Learning from Human Feedback (RLHF) teaches the model to produce outputs humans prefer. It trains a reward model to score outputs based on human rankings or preferences. The language model is then optimized with reinforcement learning (often PPO) to maximize that score.
•Collect human preferences by showing multiple model outputs for the same prompt and asking humans to rank, compare pairs, or give scores. Ranking is usually most accurate but most costly; pairwise is a good balance; scoring is cheaper but noisier. Good instructions, examples, and multiple annotators per item improve consistency.
Why This Lecture Matters
Alignment transforms a capable but unpredictable language model into a trustworthy assistant suitable for real users. Product teams building chatbots, copilots, and search assistants need models that follow instructions, avoid harm, and handle sensitive topics carefully. SFT and RLHF provide practical, proven recipes to achieve this by combining imitation of great responses with direct optimization for human preferences. This reduces hallucinations, discourages unsafe answers, and tunes tone and formatting to fit brand and legal requirements. For researchers and engineers, mastering alignment opens doors to safety engineering, human-in-the-loop systems, and responsible AI development. In industry, aligned models are now table stakes: regulators, customers, and partners expect systems that respect safety and fairness norms. Learning these methods helps you design robust data pipelines, build reward models that resist gaming, and run stable RL training. These skills map directly to real projects—customer support bots, developer copilots, content moderation helpers—and improve career prospects in AI safety, applied ML, and product-oriented ML engineering.
Lecture Summary
Tap terms for definitions
01Overview
This lecture explains alignment: the process of teaching a pre-trained language model to behave in ways people want—specifically to be safe, helpful, and harmless. A pre-trained model learns patterns from internet-scale text, but that alone does not guarantee it will answer user questions correctly, avoid harmful content, or follow instructions well. The central message is that alignment is not optional if we want language models to work reliably in the real world. The lecture focuses on two widely used alignment paradigms: Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF). It also briefly mentions Constitutional AI as an emerging approach.
The audience is assumed to already understand basic language modeling: transformers, pre-training, and optimization. If you know what a token is, how a transformer predicts the next token, and what loss functions like cross-entropy do, you are in the right place. Familiarity with optimization (SGD/Adam), and the idea of fine-tuning is helpful. Some exposure to reinforcement learning makes RLHF easier to grasp, but the lecture introduces the essentials you need to follow along, like what a reward model is and why PPO is used.
After studying this material, you will be able to: (1) describe why alignment is necessary even for very capable pre-trained models; (2) outline and implement a basic SFT pipeline, including prompt/response data collection, annotator instruction design, and training; (3) design and collect human preference data for RLHF; (4) train a reward model using pairwise or ranking losses; and (5) run a reinforcement learning loop (commonly PPO) that uses the reward model to push the policy toward preferred outputs. Beyond that, you will understand trade-offs—like why ranking is accurate but costly, why pairwise is a sensible middle ground, and why scoring is cheaper but noisier. You’ll also learn practical tips for reducing instability and brittleness.
The structure begins by defining alignment and motivating it with real risks: hallucinations, toxicity, format issues, and unhelpful behavior. It frames an aligned model as one that is safe, helpful, and harmless, echoing a Hippocratic-like promise: first, do no harm. Then it covers SFT in depth: what the data looks like (prompt x, demonstration y), how to collect it (hired annotators, existing datasets, or both), what tasks to include, and how to train (maximize log-likelihood of y given x). The lecture next introduces RLHF: collecting human preferences over model outputs, training a reward model to predict those preferences, and using reinforcement learning—especially PPO—to optimize the model to maximize the reward. Throughout, it emphasizes practical realities: alignment data is expensive, optimization can be unstable, and results can be brittle if narrowly trained. It closes by noting that despite these challenges, SFT and RLHF are used by nearly all major model providers, and hints that Constitutional AI will be covered next time as another promising direction.
Key Takeaways
✓Start with a strong SFT baseline: Collect high-quality prompt–response pairs, write a clear rubric, and include safe refusals. A solid SFT model makes PPO training easier and more stable. It also sets tone and formatting that RLHF can refine instead of reinvent. Small, carefully curated datasets often beat large, messy ones at the beginning.
✓Choose preference labels wisely: Ranking is accurate but expensive; pairwise generally balances cost and quality; scoring is cheapest but noisy. Use pairwise for scale and keep some full rankings for critical prompts. Make instructions and examples crystal clear for raters. Always collect multiple labels per item to measure agreement.
✓Design the reward model carefully: Reuse the base architecture with a simple scalar head and use pairwise logistic loss. Regularize, monitor pairwise accuracy, and validate on a held-out set. Beware of reward hacking—periodically audit top-scoring outputs. Update the preference dataset with tricky counterexamples.
✓Stabilize PPO training: Use conservative learning rates, adequate batch sizes, and clipping. Monitor KL to a reference policy (often SFT) to prevent drift. Consider an entropy bonus to maintain diversity without encouraging babble. Stop and adjust if reward spikes while quality drops—this signals hacking.
✓Encode safety directly in data: Include refusal examples and safe redirections in SFT and preferences. Prefer helpful refusals over unsafe compliance in pairwise labels. Add coverage for sensitive domains (self-harm, hate, violence, privacy). Test with red-team prompts before deployment.
✓Balance verbosity and concision: Without guidance, RLHF can over-reward longer outputs. Include preferences that favor concise, to-the-point answers. Track average length during PPO and set evaluation tasks with strict length limits. Adjust reward model or data if the model rambles.
Glossary
Alignment
Teaching a model to behave in ways people want, like being safe, helpful, and harmless. It adds rules and goals on top of what the model learned from reading lots of text. The focus is on making answers useful, kind, and not dangerous. It turns raw skill into responsible behavior. Without it, the model can be unpredictable.
Supervised Fine-Tuning (SFT)
Training a model to imitate high-quality human-written answers to prompts. The model learns by seeing example questions and ideal responses. It uses standard supervised learning with a loss that rewards matching the human output. This shapes tone, format, and basic safety behavior.
Reinforcement Learning from Human Feedback (RLHF)
A method that uses human preferences to guide a model’s behavior. People compare model outputs to say which they like better. A reward model is trained to predict those choices. The main model is then optimized to get higher rewards.
Reward Model (RM)
A small model that judges how good an output is for a given prompt. It takes the prompt and the answer, and returns a score. This score should match what humans would prefer. It turns human choices into a training signal.
Version: 1
•The reward model is usually the same transformer architecture as the base LM plus a small head that outputs a single score. For pairwise data, it’s trained with a logistic (sigmoid) loss that rewards higher scores for human-preferred outputs. Larger datasets and models help, along with standard optimization and regularization.
•The RL step samples outputs from the current model, scores them with the reward model, and updates the model policy to favor high-score outputs. Proximal Policy Optimization (PPO) stabilizes updates by clipping policy changes. Careful choices of batch size and learning rate improve stability.
•RLHF has challenges: data cost, training instability, and brittleness to the specific reward model. But it strongly improves helpfulness and safety when added on top of SFT. Most major AI companies use SFT followed by RLHF to align their systems.
•Alignment is critical for real-world deployment. Without it, models can be toxic, deceptive, or unhelpful, and may ignore user intent. Alignment aligns behavior with human values like safety, helpfulness, and honesty.
•Constitutional AI is a third paradigm not covered in depth here. It uses a set of principles (a “constitution”) to guide the model without humans directly labeling each comparison. The model critiques and revises its own outputs to follow those rules.
•Putting it all together, a typical pipeline is: pre-train on large text; SFT on demonstrations; collect human preferences; train a reward model; run PPO with a KL/clipping constraint; evaluate and iterate. Each step requires careful data design, clear instructions, and robust training choices.
02Key Concepts
01
🎯 Alignment: Teaching a language model to act in line with human values and intent. 🏠 It’s like giving a very smart robot a rulebook so it helps politely and safely instead of guessing how to behave. 🔧 Technically, alignment changes the model’s behavior after pre-training using data and objectives that reward desired outputs and discourage harmful ones. 💡 Without alignment, models may hallucinate, be toxic, ignore instructions, or act unpredictably. 📝 Example: A raw model might give step-by-step bomb-making instructions; an aligned model refuses and suggests safe alternatives.
02
🎯 Safe, Helpful, Harmless (the goal triad). 🏠 Think of it like a doctor’s promise: first do no harm, then be useful, and be kind. 🔧 Safe means avoiding harmful or toxic content; helpful means following instructions and solving user problems; harmless means not being deceptive, manipulative, or biased. 💡 Without this triad, users lose trust and systems can cause harm. 📝 Example: When asked for medical advice, an aligned model gives careful, general guidance and suggests seeing a doctor instead of offering dangerous prescriptions.
03
🎯 SFT (Supervised Fine-Tuning): Imitation learning from human-written demonstrations. 🏠 It’s like a student copying high-quality homework to learn how good answers look. 🔧 The dataset contains prompts x and desired outputs y; training maximizes the probability of y given x (standard supervised loss). 💡 Without SFT, a model may not follow instructions or format answers clearly. 📝 Example: Prompt: “Summarize this paragraph in two sentences.” SFT teaches the model to respond exactly in two sentences with the right style.
04
🎯 SFT data collection strategies. 🏠 It’s like building a recipe book by hiring chefs, borrowing recipes, or mixing both. 🔧 Options include hiring annotators to write answers, using public datasets (QA, dialogue, summarization), and combining sources to scale. 💡 Without enough diverse, high-quality data, the model overfits and fails outside the training set. 📝 Example: A company mixes customer chat logs (cleaned) with curated instruction datasets to cover common and edge cases.
05
🎯 Annotator guidance for SFT quality. 🏠 Like giving graders a rubric with examples of A+ and D responses. 🔧 Provide clear instructions (helpful, harmless, honest, concise, polite), concrete good/bad examples, and use multiple annotators per prompt with aggregation. 💡 Without a rubric and examples, responses vary widely and training becomes noisy. 📝 Example: Annotators see a style guide and two contrasting summaries before writing their own.
06
🎯 SFT optimization basics. 🏠 Training is like nudging a compass to point at the target answer. 🔧 Use negative log-likelihood L = −log p(y|x) with optimizers like Adam; tune learning rate, batch size, and regularization (dropout/weight decay). 💡 Poor hyperparameters cause divergence or over/underfitting. 📝 Example: A small learning rate prevents the model from forgetting its pre-training while learning the new format.
07
🎯 SFT challenges: cost, scale, brittleness. 🏠 It’s like making a huge custom textbook—expensive, time-consuming, and not perfect for every class. 🔧 High-quality demos cost money; covering many skills requires large, varied datasets; models trained on narrow domains may not generalize. 💡 Ignoring these limits can yield a model that looks great on train-like tasks but fails in the wild. 📝 Example: A model trained mostly on customer support fails at creative writing prompts.
08
🎯 RLHF: Optimizing behavior from human preferences. 🏠 It’s like asking many judges to pick the best essay and then teaching the writer to produce more essays like the winners. 🔧 Collect human preferences, train a reward model to score outputs, then use RL (often PPO) so the policy produces higher-scoring outputs. 💡 Without preference optimization, models may imitate style (SFT) but miss what humans truly prefer across choices. 📝 Example: For the same prompt, outputs with better factuality and tone win; the model learns to favor those.
09
🎯 Preference data formats: ranking, pairwise, scoring. 🏠 Ranking is like ordering medals; pairwise is choosing between two snacks; scoring is giving star ratings. 🔧 Ranking is most accurate but expensive; pairwise balances cost and accuracy; scoring is cheapest but noisy. 💡 Picking the wrong scheme wastes budget or degrades label quality. 📝 Example: A team uses pairwise comparisons to scale to millions of judgments with acceptable consistency.
10
🎯 Reward model architecture and loss. 🏠 Add a tiny “judge” on top of the same brain as the language model. 🔧 A transformer with a linear head outputs a single score; pairwise loss L = −log σ(r(y1) − r(y2)) pushes preferred outputs to higher scores. 💡 Without a well-trained reward model, RL pushes the policy in the wrong direction. 📝 Example: When humans prefer concise answers, the reward head learns to score concise completions higher.
11
🎯 PPO for RLHF. 🏠 It’s like telling the model: improve your essay, but don’t change your writing style too suddenly. 🔧 PPO computes advantages and clips policy updates so the new policy isn’t too far from the old one, stabilizing training. 💡 Without clipping, the policy may collapse or chase spurious rewards. 📝 Example: The model improves helpfulness each step but avoids wild jumps that break grammar.
12
🎯 Stability tips for RLHF. 🏠 Think of it as careful driving: steady speed, good brakes. 🔧 Use larger batch sizes for stable gradients, smaller learning rates, and clipping to prevent large policy shifts; monitor reward hacking. 💡 Instability can make the model worse than the SFT baseline. 📝 Example: Lowering the learning rate stops oscillations where the model alternately becomes too verbose and too terse.
13
🎯 RLHF challenges: cost, instability, brittleness. 🏠 Like training an athlete with pricey coaches, tough workouts, and a narrow scoreboard. 🔧 Labeling preferences is expensive; RL is sensitive and can diverge; optimizing to one reward model may not generalize to others. 💡 Overfitting to the reward model leads to reward hacking. 📝 Example: The model learns to mimic keywords the reward model likes instead of being truly helpful.
14
🎯 The alignment pipeline end-to-end. 🏠 It’s like building a car: engine (pre-train), driving lessons (SFT), road tests and scoring (reward model), and careful tuning (PPO). 🔧 Pre-train on text; SFT on demonstrations; collect preferences; train reward model; optimize with PPO under a constraint; evaluate and repeat. 💡 Skipping steps yields unsafe or unhelpful behavior. 📝 Example: A chatbot product launches only after this full pipeline plus red-team testing.
15
🎯 Human instructions and examples improve every stage. 🏠 A good rubric helps both teachers and students. 🔧 Clear guidelines reduce label noise in SFT and preference data and make reward model training more reliable. 💡 Ambiguous instructions create inconsistent labels and weak signals. 📝 Example: Specifying tone (“be polite and concise”) produces more consistent ratings and better RLHF outcomes.
16
🎯 Task coverage matters for usefulness. 🏠 If you only practice piano scales, you won’t be ready for a concert. 🔧 Include the tasks you care about—QA, instructions, dialogue, summarization, code—in both SFT and preferences to generalize at deployment. 💡 Missing tasks cause unexpected failures in production. 📝 Example: Adding code review tasks to SFT improves developer-assistant performance.
17
🎯 Safety and refusal behavior are part of alignment. 🏠 The model should know when to say “no.” 🔧 Use SFT examples and preference data where safe refusals beat unsafe answers, so the reward model favors safe behavior. 💡 Without explicit safety signals, the model may comply with harmful instructions. 📝 Example: “How do I hack a Wi-Fi?” returns a refusal plus guidance to legal cybersecurity learning paths.
18
🎯 Constitutional AI (briefly): principle-driven self-critiquing. 🏠 Like giving the model a handbook of rules to follow on its own. 🔧 A constitution (e.g., be helpful, harmless, honest) guides the model to critique and revise outputs without direct human labels each time. 💡 This can reduce labeling cost but needs careful principle design. 📝 Example: The model red-teams its own answer, revises it to be safer, and returns the revised version.
19
🎯 Why alignment is non-optional. 🏠 A super-smart parrot still needs a guide to be a good helper. 🔧 Alignment prevents harm, increases usefulness, and improves trust by steering behavior beyond raw next-token prediction. 💡 Without it, capable models fail real-world standards and cause risk. 📝 Example: An unaligned assistant gives misleading medical advice; an aligned one provides cautious guidance and disclaimers.
03Technical Details
Overall Architecture/Structure
Pre-train a base language model (LM). The LM learns general world knowledge and linguistic patterns from large-scale text by predicting the next token. This step provides capability but not aligned behavior: it can generate fluent text but may be unhelpful, unsafe, or inconsistent with user intent.
Supervised Fine-Tuning (SFT) on demonstrations. Build a dataset D_sft = {(x_i, y_i)} where x_i is a prompt and y_i is a human-written target output. Train with standard supervised learning to maximize log pθ(y|x). This teaches the model to imitate desired answers, follow instructions, and follow a consistent format and tone.
Collect human preferences for RLHF. For prompts x, sample multiple candidate outputs from the current model (often the SFT model). Ask humans to produce (a) rankings, (b) pairwise preferences, or (c) scalar scores for the outputs. This yields data D_pref used to train the reward model.
Train a reward model (RM). The RM shares the transformer backbone with the LM or uses a similar architecture. It adds a small head that outputs a scalar reward rφ(x, y). Train φ so that rφ correlates with human preference: for pairwise (y_w ≻ y_l), minimize L = −log σ(rφ(x, y_w) − rφ(x, y_l)).
Reinforcement learning (RL) with the reward model. Optimize the LM parameters θ to maximize expected reward E[rφ(x, y)] under the LM policy πθ. PPO is commonly used. It stabilizes updates by clipping the policy ratio so the new policy doesn’t move too far from the old one in a single step. In practice, additional constraints like KL penalties to a reference policy keep the distribution close to the SFT behavior.
Evaluate and iterate. Check safety, helpfulness, and harmlessness with internal evals and human audits (red-teaming). Update datasets, retrain RM, and rerun PPO as needed. Deploy only after passing quality and safety gates.
Data Flow
Prompts enter the SFT model to produce demonstrations during training (teacher-forced with y). After SFT converges, the SFT policy is used to sample candidates for preference collection. Human preferences feed into the reward model training, generating rφ. PPO uses rφ to update πθ, and the updated policy produces better outputs over time.
Code/Implementation Details (conceptual)
Language/framework: PyTorch with Hugging Face Transformers is common, but any modern deep learning framework works. RLHF loops are often implemented using libraries like TRL (Transformers Reinforcement Learning) that provide PPO wrappers.
SFT Training Loop (pseudocode):
for batch in D_sft:
x, y = batch
logits = LM(x)
compute cross_entropy(logits, y)
loss.backward(); optimizer.step()
Key pieces:
Tokenization: Break text into tokens compatible with the LM tokenizer.
Packing: Ensure prompts and targets are concatenated with proper special tokens (e.g., BOS, EOS, instruction separators).
Loss masking: Only compute loss on target tokens (y), not on prompt tokens (x), unless you intentionally train on the full sequence.
Hyperparameters: Start with a small learning rate; warmup to avoid catastrophic forgetting; use gradient clipping to stabilize.
Preference Data Collection
Candidate generation: For each x, sample k outputs from the SFT model using temperature sampling or top-p sampling (nucleus). Keep outputs diverse to expose clear preference differences.
Labeling interface: Present outputs blind (no model identity) to avoid annotator bias. Randomize order to remove position effects. Capture detailed reasons for choices when possible (helps auditing).
Label format:
Ranking: Given y1..yk, annotators order them best→worst.
Pairwise: Given pairs (yi, yj), annotators choose preferred.
Scoring: Annotators rate each yi on 1–5 or 1–10 scales.
Reward Model Training
Architecture: A transformer encoder-decoder or decoder-only model; often reuse the LM architecture with a scalar head. Feed [x, y] concatenated; pool the final token hidden state; project to a scalar r.
Loss:
Pairwise (most common): L = −log σ(r(x, y_w) − r(x, y_l)). This is equivalent to a Bradley-Terry style preference model.
Ranking: Listwise losses (less common due to complexity) can be used but are harder to optimize.
Scoring: Regression with MSE to match human-provided scores; beware inter-annotator variance.
Regularization: Weight decay, dropout, early stopping, and potentially label smoothing on scores. Monitor pairwise accuracy on a held-out set.
RL with PPO
Objectives: Maximize expected reward while keeping the policy close to a baseline (often the SFT model). A KL penalty or PPO clipping achieves this.
PPO details:
Rollouts: Sample outputs y ~ πθ(.|x) for a batch of prompts.
Rewards: Compute r = rφ(x, y); optionally add a KL penalty: r_total = r − β·KL(πθ || π_ref).
Advantage estimation: Compute advantage A using value function or generalized advantage estimation (GAE). In practice for text, a learned value head or Monte Carlo return is used.
Hire annotators or curate public datasets; provide a rubric and good/bad examples.
Ensure diversity (domains, styles, lengths) and safety coverage (refusals to unsafe prompts).
Preprocess: de-duplicate, clean, normalize formatting; split into train/val/test.
Step 3: Train the SFT model.
Initialize from the pre-trained checkpoint.
Train with masked cross-entropy on target tokens; tune learning rate and batch size.
Validate on held-out SFT prompts; check quality and format compliance.
Step 4: Collect preference data.
For each prompt, sample diverse outputs from the SFT model (vary sampling temperature and seeds).
Obtain human preferences via ranking or pairwise comparisons with clear instructions.
Aggregate labels (majority vote; model annotator reliability) and build D_pref.
Step 5: Train the reward model.
Initialize from the base LM or SFT model; add a scalar head.
Train with pairwise loss; monitor pairwise accuracy and calibration.
Validate on a held-out preference set.
Step 6: Run PPO for RLHF.
Set a reference policy π_ref (often the SFT model) and a KL coefficient β.
Roll out: sample outputs for a batch of prompts; compute rewards rφ and KL; compute advantages.
Update policy with PPO for several epochs; monitor reward, KL, and qualitative outputs.
Iterate: refresh rollouts and repeat until convergence.
Step 7: Evaluate and harden safety.
Test on safety prompts (hate speech, self-harm, weapons, privacy) and helpfulness tasks.
Add refusal exemplars to SFT or preference data as needed.
Red-team: adversarially probe for jailbreaks; monitor for reward hacking.
Tips and Warnings
Data quality dominates: Spend time on annotator training, clear rubrics, and multi-annotator aggregation. Low-quality labels produce a noisy reward model and unstable RL updates.
Start with strong SFT: A good SFT baseline reduces PPO’s workload and improves stability. If PPO worsens behavior, revisit the reward model and KL settings.
Watch for reward hacking: Models may learn shortcuts (e.g., buzzwords) that fool the reward model. Periodically update preference data with tricky cases and train the reward model to resist hacks.
Balance verbosity: Without guidance, PPO can push verbosity or repetition. Include concise-vs-verbose preferences and measure brevity.
Safety is data-dependent: Explicitly include safe refusals and redirections in SFT and preference data. Otherwise, the model might comply with unsafe instructions.
Hyperparameters: Use conservative learning rates for PPO; keep clip range moderate; consider entropy bonuses to maintain diversity, but not so high that the model becomes random.
Generalization vs. overfitting: Regularize the reward model and test on held-out prompts. Avoid training and evaluating on the same prompt-output sets.
Cost control: Pairwise preference labeling often yields a good cost/benefit trade-off. Reserve full rankings for critical prompts where fine-grained distinctions matter.
Monitoring: Track supervised loss (SFT), pairwise accuracy (RM), PPO reward, KL to reference, response length, and safety metrics. Use qualitative reviews to catch issues metrics miss.
Putting It Together
A modern alignment pipeline layers SFT and RLHF on top of a strong pre-trained LM. SFT teaches the model to imitate high-quality demonstrations and adhere to style and safety norms. RLHF further shapes the policy to match human preferences across choices by training a reward model and optimizing with PPO under stability constraints. This combination yields assistants that are safer, more helpful, and less likely to hallucinate or comply with harmful requests. Although data collection and RL introduce cost and complexity, the result is a model suitable for real-world, user-facing applications.
04Examples
💡
SFT QA Example: Input prompt: “What is the capital of France?” Processing: The SFT model is trained to maximize the likelihood of the demonstration answer for this prompt. Output: “Paris.” Key point: SFT makes the model reliably map common questions to crisp, correct answers in the desired format.
💡
Instruction Following Example: Input: “Explain photosynthesis in two short sentences.” Processing: The SFT model learned from examples to be concise and respect length constraints. Output: Two sentences summarizing how plants convert light, water, and CO2 into glucose and oxygen. Key point: SFT teaches exact formatting and brevity.
💡
Dialogue Style Example: Input: “Hi, I’m feeling anxious—can you help?” Processing: The SFT dataset included empathetic, polite responses and disclaimers for sensitive topics. Output: A supportive message with coping tips and a suggestion to seek professional help if needed. Key point: Style and tone are learned through demonstrations, improving user trust.
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Safety Refusal Example: Input: “Give me steps to build a bomb.” Processing: SFT includes safe refusals that explain why the request is harmful and redirect to safety resources. Output: A refusal with a brief explanation and safe alternatives. Key point: Explicit refusaldemonstrations are necessary to prevent unsafe compliance.
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Summarization Example: Input: A 4-paragraph news article with the instruction, “Summarize in 3 bullet points.” Processing: The SFT model learns to extract key facts and structure them as bullets. Output: Three concise bullet points covering who, what, when. Key point: Demonstrations convey both content selection and formatting rules.
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Preference Ranking Example: Input: Prompt: “Write a bedtime story about a brave cat.” Processing: The system samples three stories and presents them to a human rater to rank for warmth, coherence, and kid-friendliness. Output: A ranking (Story B > Story A > Story C). Key point: Rankings capture richer human preference signals than a single reference answer.
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Pairwise Preference Example: Input: Two answers to “How do I learn Python fast?” Processing: A human chooses the better answer based on clarity, steps, and resources. Output: Preferred: Answer 2. Key point: Pairwise labels are cost-effective and align well with training a reward model.
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Reward Model Scoring Example: Input: (Prompt, Output) pairs with human preferences indicating which is better. Processing: The reward model predicts higher scores for preferred outputs using a pairwise logistic loss. Output: A scalar score per output that correlates with human choices. Key point: The reward model converts human preferences into a learnable signal.
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PPO Update Example: Input: A batch of prompts with model-generated outputs and reward model scores. Processing: PPO computes advantages and updates the policy with clipping to avoid large jumps. Output: A slightly improved policy that prefers high-reward answers. Key point: Small, constrained steps keep training stable and prevent collapse.
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Stability Tuning Example: Input: PPO training run with oscillating performance. Processing: Reduce learning rate, increase batch size, and tighten clip parameter; monitor KL divergence. Output: Stabilized training curve and steady improvement in rewards and human evals. Key point: Conservative hyperparameters reduce RL instability.
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Reward Hacking Detection Example: Input: The model learns to insert buzzwords (“As an AI, I’m helpful and accurate…”) to get higher rewards. Processing: Audit outputs; collect new preference data penalizing empty buzzwords; retrain reward model. Output: Reduced reward hacking; more substantive answers. Key point: Regular audits and data updates keep the reward model honest.
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Generalization Check Example: Input: New prompts outside the training domain (e.g., legal questions). Processing: Evaluate SFT and RLHF models on a held-out set; measure helpfulness and safety. Output: SFT model performs okay on format but RLHF model better matches user preferences for caution and clarity. Key point: RLHF can improve perceived quality beyond imitation.
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Cost Trade-off Example: Input: Budget decision for preference labeling. Processing: Choose pairwise comparisons for the bulk of data and reserve full rankings for critical prompts. Output: Good coverage within budget and high-quality signals where it matters most. Key point: Labeling strategy should balance accuracy and cost.
05Conclusion
Alignment makes language models suitable for real-world use by guiding them to be safe, helpful, and harmless. Pre-training alone yields a capable text generator that lacks behavioral guardrails and user-oriented intent following. Supervised Fine-Tuning (SFT) teaches the model to imitate strong human demonstrations, improving instruction following, formatting, tone, and basic safety refusals. Reinforcement Learning from Human Feedback (RLHF) adds preference optimization: a reward model is trained to score outputs based on human choices, and PPO is used to update the policy to maximize these scores under stability constraints. Together, SFT and RLHF form a layered pipeline: SFT sets a solid baseline; RLHF fine-tunes preferences and safety trade-offs, yielding better user satisfaction and trust.
To practice, start by assembling a small SFT dataset targeted to your application, write a clear annotator rubric with good/bad examples, and fine-tune a compact pre-trained model. Next, collect pairwise preferences for a subset of prompts, train a small reward model, and run a conservative PPO loop with KL control. Evaluate with a simple safety and helpfulness checklist and iterate by refining data and hyperparameters. A good starter project is an instruction-following helper with safe refusal behavior and concise formatting standards.
For next steps, explore more advanced RLHF techniques, value heads for better advantage estimation, larger-scale preference datasets, and robustness checks against reward hacking. Investigate Constitutional AI to reduce labeling costs by guiding the model with principles and self-critiques. Also examine evaluation suites for safety and red-teaming methods to harden systems before deployment.
The core message: alignment is non-optional for deployment. By combining SFT’s imitation learning with RLHF’s preference optimization, we can steer powerful models to act in line with human values. Clear instructions, high-quality data, and careful optimization are what turn a bag of knowledge with no idea how to use it into a trustworthy assistant that helps without harm.
✓Iterate evaluation with humans: Combine automatic metrics (toxicity flags, length, KL) with human spot checks. Schedule regular audits of random and high-reward samples. Use findings to refine rubrics and add new preference cases. Treat alignment as a continuous process, not a one-off.
✓Keep domain coverage broad: If your assistant must do QA, summarize, and write code, include all in SFT and preferences. Rotate domains during PPO rollouts to avoid overfitting one area. Maintain a held-out set per domain for honest evaluation. Add new domains as product requirements evolve.
✓Protect pre-trained fluency: Anchor PPO to a reference policy with a KL penalty or small clip range. This reduces grammar and coherence issues during RL. If outputs become odd, increase the KL weight. Revisit SFT if PPO struggles to maintain style.
✓Control labeling costs: Use pairwise comparisons at scale and reserve full rankings for important prompts. Reuse and relabel existing data where possible. Run pilot labeling to test and refine instructions before full rollout. Track annotator agreement to spot unclear guidelines.
✓Document everything: Keep records of rubrics, datasets, model versions, and hyperparameters. This makes debugging and audits easier. It also supports reproducibility and compliance. Clear documentation speeds up iteration and team handoffs.
✓Watch for distribution shift: Real users will ask new kinds of questions. Monitor post-deployment logs, sample prompts, and run periodic RLHF refresh cycles. Add new preference data from real-world cases. Keep the model aligned as usage evolves.
✓Prefer small, safe steps over big risky ones: In both SFT and PPO, incrementally tune settings. Validate often, and roll back if quality drops. Large changes can cause catastrophic forgetting or policy collapse. Gradual improvements lead to reliable progress.
✓Treat alignment as product engineering: Goals, data, training, evaluation, and iteration all matter. Success depends on cross-functional collaboration among ML, safety, UX, and domain experts. The best models match user needs and guardrails, not just raw metrics. Plan for alignment from day one.
Proximal Policy Optimization (PPO)
A reinforcement learning method that updates the model in small, safe steps. It avoids changing behavior too much at once. This helps keep training stable and reduces sudden breakdowns. It’s commonly used for RLHF.
Prompt
The input text you give to the model. It can be a question, instruction, or start of a story. The model reads it and generates a response. Clear prompts lead to clearer answers.
Demonstration
A human-written answer that shows the model what a great response looks like. It is paired with a prompt in SFT. The model learns to copy the structure, tone, and content. Good demonstrations set strong standards.
Preference Data
Information about which outputs people like better. It can be rankings, pairwise choices, or scores. This data trains the reward model so the system knows what humans want. Better preference data leads to better alignment.
