RLHF OUTSOURCING SERVICES

AI systems are becoming more integral to decision-making and are reaching a broader user base than ever before. Issues like fragmented datasets, human biases, and incomplete algorithms can undermine output fairness and accuracy. BUNCH addresses these challenges through Reinforcement Learning from Human Feedback (RLHF), ensuring your models deliver trustworthy, accurate, and fair results.
‍

Human-Centered Feedback

Today, reviewing LLMs with Reinforcement Learning from Human Feedback (RLHF) is essential. Our specialists are trained in both reward-function and constitutional AI techniques, guiding LLMs with human preferences to make informed output decisions. Our diverse evaluators ensure that outputs are balanced and inclusive of all user interactions.

Through our expertise in RLHF and data annotation, we rigorously assess datasets for underrepresentation, balance, and completeness. Our ongoing safety checks and ethical standards are integrated into all aspects of our RLHF services, ensuring that your model performs reliably while fostering broader user engagement and trust.

The foundation of Reinforcement Learning from Human Feedback (RLHF), used in OpenAI's InstructGPT, shows how building a strong policy and reward function is crucial for both safety and success in AI models. Your LLM’s reward function needs to align its learning process with your goals while avoiding common pitfalls that many companies encounter.

Reward functions have become increasingly complex, and even with good intentions, AI can misinterpret them. BUNCH’s RLHF services optimize reward functions, aligning your AI’s behavior with your company’s objectives and ensuring it responds appropriately to real-world data.

We work closely with you to define clear goals for your AI model, keeping it aligned with its true purpose. Our team is trained in AI safety and adapts to your specific training, brand guidelines, and expectations. Whether you have existing policies or need to develop new ones, we collaborate with your internal team to calibrate your LLM for success.
‍

Feedback Loops

Continuous human feedback is at the core of refining reward functions. We adjust outputs in real-time, preventing harmful or unexpected behaviors. Through supervised fine-tuning (SFT) and policy optimization, we keep your AI aligned with our shared goals.
‍

Reward Engineering

We develop dynamic reward functions to adapt your model to shifting contexts. BUNCH’s safety team fine-tunes your AI, balancing multiple objectives and ensuring top performance. As your model evolves with new data and user preferences, we keep it compliant with regional regulations while safeguarding your brand integrity.

Regular reviews of AI models and prompts guarantee alignment with your model’s purpose and industry standards. Ethical guidelines are integrated at every stage, with guardrails for sensitive user interactions. Our monitoring and ethical audits help keep your AI safe and secure as it grows.