How long does an AI implementation take?

Most single workflow implementations take 2-6 weeks from kickoff to production. Full AI transformation programmes run 6-12 weeks.

Do you work with specific AI models?

We are model-agnostic and work with all major providers including Anthropic Claude, OpenAI GPT, Google Gemini, Meta Llama, Mistral, and more.

Can you deploy AI on our own servers?

Yes. Our Local & Private AI service deploys models on your own infrastructure or private cloud.

Glossary

Reinforcement Learning

Reinforcement learning (RL) is a machine learning paradigm where an agent learns optimal behaviour through trial and error, receiving rewards or penalties for its actions and improving its strategy over time.

What is Reinforcement Learning?

Reinforcement learning is a training approach where an AI agent learns by interacting with an environment and receiving feedback in the form of rewards or penalties. Unlike supervised learning (which learns from labelled examples) or unsupervised learning (which finds patterns in data), reinforcement learning learns from experience — discovering which actions lead to good outcomes through exploration. The most familiar successes of reinforcement learning include DeepMind's AlphaGo (which defeated the world champion at Go) and robotic control systems that learn to walk, grasp objects, and navigate environments. In the context of large language models, reinforcement learning from human feedback (RLHF) has become the critical technique for aligning model behaviour with human preferences.

How Reinforcement Learning Works

A reinforcement learning system consists of an agent, an environment, actions, states, and rewards. The agent observes the current state of the environment, chooses an action, receives a reward (positive or negative), and transitions to a new state. Through many iterations, the agent learns a policy — a mapping from states to actions — that maximises cumulative reward. In the context of LLMs, RLHF works by first training a reward model from human preferences (humans rank different model outputs from best to worst). The language model is then optimised using reinforcement learning to generate outputs that the reward model scores highly. This process makes models more helpful, honest, and safe. Recent advances include RLHF alternatives like Direct Preference Optimisation (DPO) and Reinforcement Learning from AI Feedback (RLAIF), which simplify the process while achieving comparable results.

Why Reinforcement Learning Matters for Business

RLHF is the technique that transformed raw language models into the helpful, conversational assistants that businesses deploy today. Without reinforcement learning, language models would generate text that is statistically plausible but not necessarily helpful, safe, or aligned with user intentions. Beyond LLM alignment, reinforcement learning powers business applications in optimisation — supply chain management, pricing strategies, resource allocation, and scheduling. Any decision-making process that involves sequential choices with measurable outcomes is a candidate for reinforcement learning. For organisations deploying AI, understanding RLHF helps explain why different versions of the same base model can behave very differently. The RLHF process is what differentiates a raw model (which might generate anything) from a refined product (which reliably follows instructions and avoids harmful outputs).

Practical Applications

Beyond LLM training, reinforcement learning is applied in recommendation systems (learning to suggest content that maximises engagement), robotics (learning motor control and manipulation), autonomous vehicles (learning driving policies), game AI (learning strategies in complex environments), and resource optimisation (learning to allocate computing resources, manage energy grids, or optimise logistics). In enterprise AI, reinforcement learning is increasingly used for dynamic pricing, personalised marketing, and automated trading strategies — any domain where sequential decisions must be optimised based on outcomes.

Related Terms

Explore further

llm fine tuning neural network ai agents guardrails

FAQ

Frequently asked questions

RLHF (Reinforcement Learning from Human Feedback) is the process of training language models to align with human preferences using reinforcement learning. It is how models learn to be helpful, follow instructions, and avoid harmful outputs. Virtually all major commercial LLMs use RLHF or similar techniques.

Most business AI applications use models that have already been trained with RLHF. You rarely need to implement reinforcement learning yourself unless you are building optimisation systems, robotics controllers, or other applications that involve sequential decision-making with measurable rewards.

Supervised learning requires labelled examples (input-output pairs) and learns to replicate those mappings. Reinforcement learning learns through trial and error with reward signals, discovering optimal strategies without being shown the correct answer. RL is suited for sequential decision-making where the correct action depends on context.

Large Language Models

How RLHF shapes the behaviour of modern LLMs.

Fine-Tuning

Other techniques for adapting model behaviour.

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