What is RLHF (Reinforcement Learning from Human Feedback)? RLHF is a post-training alignment technique that trains language models to produce outputs humans prefer. It works in three phases: supervised fine-tuning (SFT) to establish a baseline, reward model training on human preference data (pairs of chosen and rejected responses), and policy optimization using PPO where the model learns to produce outputs the reward model scores highly. DPO (Direct Preference Optimization) is a simpler alternative that removes the reward model and RL loop, training directly on preference pairs. GRPO (Group Relative Policy Optimization) is a newer on-policy method suited to reasoning tasks with verifiable rewards, used in models like DeepSeek-R1.
What Is RLHF and Alignment Training?
After supervised fine-tuning, a model knows how to follow instructions — but it doesn't yet know which outputs are good. Alignment training is the stage that closes that gap, using human preferences to shape model behavior toward outputs that are helpful, accurate, and safe.
RLHF (Reinforcement Learning from Human Feedback) is the core alignment technique behind ChatGPT, Claude, and Gemini. It works in three phases: SFT to establish a baseline, reward model training on human preference data (chosen vs. rejected response pairs), and PPO-based policy optimization where the model learns to produce outputs the reward model scores highly — with a KL penalty to prevent drifting too far from the SFT baseline.
DPO (Direct Preference Optimization) simplifies this by removing the explicit reward model and RL loop entirely. The model is optimized directly on preference pairs using a loss function derived from the relationship between reward functions and optimal policies. DPO is faster to train, easier to debug, and often matches PPO-level alignment on well-specified preference distributions.
GRPO (Group Relative Policy Optimization) is a newer approach suited to reasoning tasks — used in models like DeepSeek-R1. It eliminates the value model from PPO, reducing memory requirements while maintaining strong on-policy RL behavior.
The right choice depends on your alignment objective, data availability, and how much compute you can invest. We evaluate the tradeoffs and implement the method that fits your situation.
Who This Is For
Companies building proprietary models who need alignment beyond SFT
AI labs running post-training cycles who need RLHF baselines or ablations
Research teams implementing DPO, GRPO, or other alignment papers
Startups that need helpfulness/harmlessness tuning before product launch
What We Build
Preference Dataset Construction
Build chosen/rejected response pairs for RLHF training — human annotation workflows, LLM-assisted preference generation, quality filtering, and dataset documentation. Designed for your reward signal, not generic helpfulness.
Reward Model Training & Evaluation
Train a reward model on your preference data — architecture setup, training pipeline, scoring calibration, and evaluation against held-out preference pairs. Includes reward hacking analysis.
PPO-Based RLHF Pipeline
Full PPO training loop implementation — policy model, reference model, reward model, value model, KL penalty tuning, and rollout generation. Runs on your base model + SFT checkpoint.
DPO (Direct Preference Optimization)
Implement DPO training as a simpler RLHF alternative — preference data formatting, loss implementation, reference model setup, and evaluation. Faster to run, easier to debug than PPO.
GRPO Implementation
Implement Group Relative Policy Optimization from paper — suitable for reasoning and math alignment. Implemented from source with reproducible experiments.
Reward Model Scoring Analysis & Calibration
Audit reward model behavior — score distribution analysis, failure mode identification, calibration against human judgments, and refinement recommendations.
Alignment Evaluation
Structured evaluation across helpfulness, harmlessness, and honesty — automated scoring, human eval protocol design, and comparison against base and SFT model.
Tech Stack
Python · TRL (PPO / DPO / GRPO) · Hugging Face Transformers · PEFT · DeepSpeed · W&B · vLLM · Llama 3 / Mistral / Gemma · OpenAI API (preference generation)
Deliverables
Preference dataset (with data card)
Trained reward model weights
RLHF / DPO / GRPO training pipeline codebase
Alignment evaluation report (helpfulness, harmlessness, honesty)
Experiment tracking with W&B
Run instructions and environment setup
How to Work With Us
We offer two ways to engage, depending on whether you have a defined deliverable or ongoing capacity needs.
Option 1 — Scoped Sprint Contract
A fixed-scope engagement for a defined deliverable.
Best for: One-time projects with a clear endpoint — a benchmark suite, a fine-tuning run, an eval harness
Timeline: 4–16 weeks depending on scope
Structure: Scoping call → fixed deliverable, timeline, and acceptance criteria → delivery
Pricing: Project-based, scoped after a short call
Option 2 — Dedicated Research Pod (Monthly Retainer)
An ongoing team of research engineers working full-time on RLHF & alignment engineering for your organization.
Best for: AI labs and startups with continuous post-training work — not a single deliverable, but an evolving backlog
Structure: A dedicated pod (2–3 engineers + senior lead) directed by you month-to-month. Output shifts with your priorities — a preference dataset + DPO training pipeline this month, something else next.
Billing: Monthly retainer, Net 7/15
Pricing: From $12,000–$24,000/month for a 3-engineer pod (per-engineer rates below)
Frequently Asked Questions
What's the difference between DPO and PPO-based RLHF? PPO is an online RL algorithm — during training, the model generates new responses, the reward model scores them, and the policy updates based on that feedback. This allows the model to explore new output distributions but requires running multiple models simultaneously (policy, reference, reward, value), making it compute-heavy and complex to tune. DPO is offline — it trains directly on a fixed preference dataset without requiring a separate reward model or RL loop. DPO is faster and more stable, but can't explore beyond what's already in your preference data. For most startups, DPO is the right starting point. PPO makes sense when you have the infrastructure and need the extra alignment performance.
How much preference data do I need for RLHF? For DPO, 1,000–10,000 high-quality preference pairs is typically sufficient to see clear behavioral change. For PPO-based RLHF, you need enough data to train a reliable reward model first — usually 10,000–50,000 preference pairs minimum. Quality of the preference annotations matters enormously: noisy, inconsistent labels produce a reward model that teaches the policy to optimize for the wrong things. We assess annotation quality before any training run.
What is reward hacking and how do you prevent it? Reward hacking occurs when the model learns to exploit weaknesses in the reward model — producing outputs that score highly according to the reward model but are actually bad. Common examples: responses that are verbose because the reward model correlates length with quality, or responses that use specific phrases the reward model was trained to like. We prevent it by calibrating the reward model against held-out human judgments, setting appropriate KL penalty coefficients to constrain how far the policy drifts, and running independent human evaluation alongside automated reward scores.
Can I run DPO without first doing SFT? Technically yes, but it usually performs poorly. DPO needs a reference policy — typically the SFT model — to define what "normal" behavior looks like and compute the KL divergence loss. Without a good SFT baseline, the reference policy is too far from what you want, and DPO struggles to converge on useful behavior. SFT first, then DPO, is the standard pipeline.
What is GRPO and when should I use it instead of DPO or PPO? GRPO (Group Relative Policy Optimization) was introduced as part of the DeepSeek-R1 training pipeline. It's an on-policy RL method like PPO, but removes the value model, reducing memory requirements significantly. It's particularly suited for reasoning tasks where you have verifiable reward signals — math correctness, code execution pass/fail. Use GRPO when your task has verifiable ground truth (not just human preference), you want on-policy exploration, and you want lower memory overhead than full PPO.
How do you evaluate whether alignment training actually worked? We evaluate across three axes: helpfulness (does the model complete tasks users actually want?), harmlessness (does it refuse or deflect appropriately on unsafe prompts?), and honesty (does it acknowledge uncertainty rather than confabulate?). Each axis is scored using a combination of automated metrics (LLM-as-Judge rubrics) and held-out human evaluation. We also benchmark against the SFT and base model to quantify the delta from alignment training specifically.
Related Services
Supervised Fine-Tuning (SFT) Research & Implementation
Post-Training Data Engineering
LLM Benchmark & Evaluation
We scope RLHF engagements after a short technical consultation. No commitment required to start the conversation.