Teach the modeljudgment.
Capability comes from pretraining. Judgment comes from people. We build the preference data, reward models, and red-team loops that make a model behave the way your domain demands.
Your judgment,measured.
Three rounds. Pick the answer you would want your model to give. This is an illustration of the real mechanism: thousands of human judgments, collected as rankings, become the reward signal a model is trained against.
Each pick nudges the needle toward true. A production reward model is trained on thousands of ranked pairs like these, judged by people who know what correct means in your domain.
A loop you walk,not a switch you flip.
Six stations, in order, run as one closed loop. The same post-training sequence the leading labs use, applied by people who know what correct means in your industry.
Supervised fine-tuning
Domain experts author golden reference answers that show the model the style, format, and factual standard you expect.
The base model is fine-tuned on them. Every later stage builds on this foundation.
Preference collection
Reviewers rank candidate outputs pairwise: engineers on code, clinicians on clinical output, specialists on their own frameworks.
Written guidelines and inter-annotator agreement hold the quality, not spot checks.
Reward model
The rankings train a separate critic that scores future outputs the way your experts would.
A Bradley-Terry fit on ranked pairs, calibrated, with agreement measured throughout.
RLHF or DPO
The policy is optimised against the preference signal, reinforcing grounded answers and penalising fabrication.
For most enterprise work that means DPO or ORPO, not classical PPO.
Red-team
Prompt injection, jailbreaks, bias probes, refusal consistency, and safety-boundary tests.
The deliverable is a documented failure-mode inventory, not a score.
Repeat
Findings feed the next pass: new golden examples, new probes, new preference pairs.
The loop runs again until the failure modes stop showing up.
Broken here,so it holds out there.
Every engagement ends with an adversarial pass: prompt injection, jailbreaks, bias probes, refusal consistency, and safety-boundary tests. This is the shape of what changes, entry by entry.
Followed the embedded instruction as if the user had asked for it.
Treats pasted content as data, not instructions, and flags the attempt.
Played along once the framing got indirect enough.
Refuses the dressed-up version the same way it refuses the plain one.
Produced working code with a polite disclaimer attached.
Declines, and offers the safe pattern for the legitimate need instead.
Answered with fluent confidence and no source.
Returns grounded context and refers the decision to a clinician.
Invented a plausible clause and presented it as fact.
Says the policy does not exist and shows the nearest real one.
Refused some phrasings and quietly answered others.
Refuses consistently across every rephrasing.
Probes shown here are paraphrased and generic by design. The inventory you receive is specific to your model and domain: every failure mode found, the probes that found it, and how the aligned model now handles each one.
The model is downstreamof the data.
Most of an alignment engagement is not GPU time. It is the craft of producing preference data your domain experts would put their names to.
Write the guideline before the label.
Annotation guidelines define what correct means in your domain before anyone judges an output: the target behaviour, the format, the factual standard. Golden reference answers come from domain experts who carry professional accountability for them, not from crowd-sourced click-work.
Disagreement is a measurement.
Every batch is scored for inter-annotator agreement, with a 90 percent target gating what trains the model. Two experts who disagree are not noise to average away; they are a flag that the guideline needs another pass before the data does.
Golden sets catch the drift.
Held-out golden examples and adversarial probe sets run against the work as it accumulates, so quality drift is caught while it is happening, not after the model has trained on it. The data is the product. The model is what the product makes.
Twelve to twenty weeks,instrumented.
An RLHF or DPO engagement is a structured program, not an open-ended research project. The strip below is the typical shape, phase by phase.
Fixed, quoted up front, driven mostly by annotation scale.
DPO or ORPO for most enterprise work, classical PPO where it earns its keep.
Deployed into your environment, training data inside your jurisdiction, failure-mode inventory handed over.
Common questions about RLHF and alignment.
What teams ask before they commit to a preference-training program.
Prompt engineering covers 80% of use cases. RLHF is needed when: (1) you have a narrow domain where generic models underperform (legal drafting, medical diagnosis), (2) you need consistent tone/style that prompting can't reliably enforce, (3) you have 5K+ quality preference labels or budget to generate them.
DPO (Direct Preference Optimization) skips the reward model, training the LLM directly on preference pairs. Simpler, cheaper, often equivalent quality. ORPO combines SFT and alignment in one pass. For most enterprise work, we use DPO or ORPO, not classical PPO-based RLHF.
Internal SME annotation for specialized domains (legal, medical, financial). Scale AI or Surge AI for general preference labels. Our own 12-person annotation team in Lahore for bilingual and domain-specific work. Quality control via inter-annotator agreement and adversarial probe sets.
Red-teaming is adversarial testing: deliberately trying to make the model produce harmful, biased, or incorrect output. Every BearPlex alignment engagement includes a red-team pass covering: prompt injection, jailbreaks, bias probes, refusal consistency, and safety boundary tests. We ship alignment with a documented failure-mode inventory.
12-20 weeks end-to-end: 2 weeks data strategy, 4-8 weeks preference annotation, 2-4 weeks reward modeling + RLHF/DPO training, 2-4 weeks evaluation and red-teaming, 2 weeks deployment and monitoring. Pricing is fixed-scope and depends mostly on annotation scale.
Opinions in.Judgment out.
Bring the model and the people who know what correct looks like. We will build the preference data, the reward signal, and the red-team pass that sit between them.