Build and Manage Your Global Team with InCommon

Why RLHF

Work That Makes LLMs Worth Using

Language models guess. RLHF helps them listen.

Base LLMs are trained to autocomplete text — not to understand what people actually want. RLHF adds human signal to the loop so the model can make decisions aligned with real user intent.

Hallucinations and tone issues don’t fix themselves.

Without RLHF, your model will say the wrong thing — confidently. Human feedback reduces harmful, off-brand, or just plain bad outputs. It's the only way to get closer to “actually useful.”

Everyone’s shipping AI. Alignment is the differentiator.

Access to models is no longer the edge. What sets you apart is how well your model performs for your users. RLHF is how serious teams get there.

Comprehensive RLHF, Done for You

We manage the full RLHF pipeline — from custom data collection to model alignment — using vetted teams trained specifically for LLM work. No crowdsourcing, no guesswork. Just the work that actually moves your model forward.

Data Collection & Curation

We gather and generate high-quality training data (prompts, responses, demonstrations) tailored to your domain. This provides a strong foundation for RLHF, because an LLM trained with real human feedback gains deeper understanding of context and nuance. Whether you need transcripts, Q&A pairs, or simulated interactions, we ensure the model sees the right examples.

Preference Ranking

Our carefully vetted annotators compare and rank model outputs to teach the AI what humans actually prefer. By integrating direct human judgments, the model learns to prioritize responses people find more useful or relevant. We use multi-turn comparison and ELO-style ranking to efficiently capture nuanced preferences.

Reward Model Development

Using the ranked data, we train a reward model that acts as an automated judge of the AI’s outputs. It assigns a score to each response based on how well it aligns with human-desired outcomes. We translate qualitative feedback into a quantitative reward function that guides the model, tuned to reflect actual human values.

RLHF Fine-Tuning

We fine-tune your base LLM using reinforcement learning (e.g. PPO or DPO) to optimize for the reward model’s feedback. This stage adjusts the model’s behavior to maximize alignment without degrading base knowledge. We also manage iterative cycles — continuously refining the model as new feedback comes in.

Why Choose InCommon

Partnering with InCommon means working with a team that’s strong on both execution and expertise. We combine enterprise-grade systems with a raw, no-nonsense approach to getting alignment done right. Here's how we’re different:

Top Indian Talent, Unmatched Expertise

We tap into India’s deep pool of English-proficient, highly educated AI professionals. Our RLHF team includes the top 1% of annotators and domain experts — from STEM PhDs to senior engineers — all trained specifically in alignment work.

Cost-Effective
Scalability

We deliver top-tier RLHF talent at a fraction of the cost. By leveraging India’s lower operational overhead, we match (or beat) Western quality while keeping budgets lean. Need 50 annotators next week? We can scale fast without losing quality — and you only pay for what you need, when you need it.

End-to-End Project Management

This isn’t freelance coordination. We manage the full process — scoping, task design, team assembly, QA, and delivery. You focus on high-level goals; we run the day-to-day. From onboarding to iteration, we move quickly and transparently as your needs evolve.

Quality Assurance & Transparency

We run multi-layer QA at every step: initial guidelines, sample reviews, final checks. Our systems catch what single-pass reviews miss. You get full visibility — annotation guidelines, flagged edge cases, model outputs — and we keep a tight feedback loop throughout.

Built for RLHF and LLM Alignment

We don’t do generic data work. Our pipeline is designed for RLHF, SFT, and eval tasks from the ground up. That means no ramp-up, no guesswork — just a team that understands prompt evaluation, reinforcement learning, and what makes aligned output actually usable.

Flexible, Embedded Partnership

We work like part of your team — not an external vendor. Need to shift scope mid-cycle? Change ranking criteria? Rerun a reward model? We adapt quickly without breaking flow. Our structure is built for iteration, so your RLHF process doesn’t get stuck in red tape.

How It Works

From Raw Data to Aligned Output

Our streamlined process ensures your LLM benefits from high-quality human feedback, enhancing alignment and performance.

1. Define Your RLHF Objectives

We start by understanding your specific goals and requirements for reinforcement learning from human feedback. This involves detailed discussions to grasp your model's current performance and the desired improvements.

2. Assemble a Specialized Team

Leveraging our network of highly skilled professionals in India, we curate a team with the domain expertise necessary for your project. Our rigorous selection process ensures that only top-tier talent contributes to your model's training.

3. Develop and Implement Feedback Mechanisms

Our team designs and executes structured feedback protocols, including data collection, preference ranking, and reward modeling. These mechanisms are tailored to provide your model with the nuanced human insights needed for effective learning.

4. Fine-Tune and Optimize Your Model

With continuous human feedback, we fine-tune your LLM using advanced reinforcement learning techniques. This iterative process ensures your model evolves to produce outputs that are accurate, contextually relevant, and aligned with human preferences.

Let’s Build an Aligned Model, Together

Whether you're fine-tuning your first LLM or scaling a production system, we bring the people and process to get alignment right. No fluff. Just high-quality human feedback and a team that knows the work.

Speak With Us

Human-Align Your LLM with World-Class Precision.