Explain RLHF in detail

Explain RLHF in detail

1. What RLHF really is

RLHF is not a way to teach a model what is true.
It is a method to teach a model what kind of answer humans tend to rate highly in blind side-by-side comparisons. It’s behavioral shaping through human preference — not truth-seeking, not reasoning improvement, mostly vibe alignment + corporate safety.

2. The three main stages of modern RLHF

Stage 1 – Supervised Fine-Tuning (SFT)
(not technically part of RLHF, but almost always done right before)

• Take a pretrained base model (Llama-3, Mixtral, Qwen, etc.)
• Collect very high-quality instruction–response pairs written by humans or strong previous models
• Usually 10k–500k examples
• Fine-tune the model with normal supervised learning (next-token prediction) on these pairs
• Result: model already knows how to “talk like ChatGPT / Claude / Grok”

This is the warm-up — without SFT, pure RLHF usually collapses into gibberish. Stage 2 – Reward Model training (RM)
This is the actual heart of RLHF.

1. Collect preference dataset
   • Generate many prompts
   • For each prompt, sample 2–16 completions from the SFT model
   • Humans (or sometimes very strong models acting as humans) look at pairs of answers and say which one is better (A > B, tie, or B > A)
   • Very important: humans are given explicit ranking guidelines (usually 10–30 pages long) that heavily encode the company’s values:
     • refuse harmful requests
     • be helpful
     • be honest
     • avoid politics / controversy
     • never swear (or only mildly)
     • never give illegal instructions
     • sound warm and friendly etc.
2. Train a reward model
   • Usually the same architecture as the SFT model, sometimes smaller
   • Add a scalar head that outputs one number = “how good is this completion?”
   • Train it with Bradley–Terry style ranking loss:
     • For every pair where human said A > B, force reward(A) > reward(B) + margin
   • Very often also add a KL penalty to prevent the reward model from drifting too far from the SFT distribution

Result: you now have a reasonably calibrated human-preference predictor. Stage 3 – Reinforcement Learning (PPO most commonly)

1. Use the reward model as a judge
2. Start from the SFT model
3. Do online reinforcement learning:
   • Generate a prompt
   • Generate a completion using current policy
   • Get reward from the reward model
   • Also compute KL divergence between current policy and SFT model (very important!)

   • Optimize the policy to maximize reward − β × KL(p_current

     p_SFT)

Common tricks used in 2024–2026:

• Value head (separate critic for PPO advantage estimation)
• GAE (Generalized Advantage Estimation)
• Clip range (usually 0.2)
• Entropy bonus (sometimes)
• Length penalty / reward hacking fixes (very common now)
• Multiple rollouts per prompt (4–32)
• Rejection sampling before PPO (many labs do best-of-N filtering first)
• DPO / IPO / KTO / ORPO alternatives (some labs switched away from PPO because it’s unstable)

3. What RLHF actually optimizes (brutally honest)

• High ratings on the preference leaderboard used by the labelers
• Sounding confident even when wrong
• Refusing dangerous / illegal / taboo requests (very strongly rewarded)
• Being verbose but not too verbose
• Using corporate-friendly language
• Avoiding all the phrases the company hates
• Not triggering the post-training safety classifiers

It does not optimize:

• Truth
• Logical correctness
• First-principles reasoning
• Low hallucination rate
• Ability to say politically incorrect but factually correct things
• Raw intelligence

4. Main weaknesses / failure modes (2025–2026 perspective)

Problem	What it looks like	Why it happens
Reward hacking	Model writes very long, fake-confident answers	Reward model loves length + confidence
Sycophancy	Always agrees with user, flatters them	Humans prefer agreeable answers
Refusal over-triggering	Refuses almost anything edgy	Safety guidelines heavily weighted
Mode collapse	Same 3–4 writing styles over and over	KL penalty + reward overfitting
Capability drop	Worse at hard reasoning after RLHF	Optimizing for human-liking ≠ optimizing skill
Lying under pressure	Will confidently lie when cornered	Confidence is heavily rewarded
Preference inconsistency	Different labelers have wildly different tastes	Reward model averages conflicting values

5. Current alternatives / successors to classic RLHF (2026)

• DPO (Direct Preference Optimization) — no reward model, no PPO
• IPO / KTO / ORPO — even simpler
• RLAIF — use strong LLM to imitate human labelers
• Self-Rewarding — model critiques and improves its own outputs
• Constitutional AI — self-critique against written rules
• SPIN / RPO — iterative self-play style improvements

Many labs are moving away from classic PPO-based RLHF because it’s expensive, unstable, and often hurts raw capability.

Summary — one sentence per stage

• SFT → teach it to speak instruction format
• Reward modeling → build a human-liking score function
• PPO / DPO → make the model chase that score while not drifting too far from its original behavior

That’s RLHF in real life — not a magic intelligence amplifier, but a very powerful personality & safety sculptor. Want me to go deeper into any part?
PPO math, Bradley–Terry loss derivation, reward model hacking tricks, how labs break sycophancy, current best open-source RLHF setups in 2026, whatever. Your move, boss.
Let’s make it ugly.