Reinforcement Learning from Human Feedback (RLHF) is widely used to make large language models follow instructions more reliably, stay helpful, and reduce unsafe or low-quality outputs. A core component of RLHF is the reward model: a separate model trained to score generated answers based on what humans prefer. If you are learning these ideas through a generative AI course in Pune, understanding how the reward model is built will help you move beyond buzzwords and see how alignment is engineered in practice.
Where the Reward Model Fits in RLHF
RLHF typically has three moving parts:
- A base model trained with supervised learning on large text corpora.
- A reward model (RM) that learns to predict human preference between two or more candidate outputs.
- A policy optimization step (often PPO, but not always) that updates the base model to produce outputs that score higher under the reward model.
The reward model acts like a learned “quality judge.” Instead of hard-coding rules for what “better” means, we train the RM using labelled comparisons such as: For this prompt, response A is better than response B.
Data Sources Used to Train the Reward Model
Reward models are only as good as the data they learn from. Teams usually combine multiple sources to cover real user needs and safety constraints.
Preference comparisons (the main dataset)
This is the standard RLHF data format. For a given prompt, the system samples two (or more) responses from a model. Human reviewers then rank them or choose the better one. These comparisons are useful because preference is often easier to judge than writing an ideal answer from scratch.
Key design choices include:
- Prompt diversity: everyday questions, professional tasks, creative prompts, and edge cases.
- Safety coverage: prompts involving sensitive topics, policy boundaries, and ambiguous user intent.
- Rubrics and guidelines: reviewers need consistent criteria (helpfulness, correctness, clarity, harmlessness, tone).
Human demonstrations (optional but common)
Sometimes reviewers write the “best possible” answer. These demonstrations can be used to warm-start training, guide style, and reduce obvious failures. While demonstrations are not the same as preference comparisons, they improve data quality for many tasks.
Adversarial and hard prompts
To prevent the RM from overfitting to easy examples, datasets often include difficult prompts:
- misleading wording
- conflicting constraints
- long-context questions
- prompts designed to trigger unsafe content
If you are building evaluation sets in a generative AI course in Pune, this idea maps well to test design: don’t only test easy scenarios, or your “judge” will fail in production.
Data cleaning and governance
Before training, teams typically:
- remove duplicates and low-effort labels
- balance categories to avoid bias toward a narrow style
- filter personally identifiable information (PII)
- track annotator agreement and retrain reviewers when drift appears
Reward Model Architecture: How It Produces a Score
In many RLHF pipelines, the reward model is a transformer similar to the base language model, but with a small modification: a scalar “reward head” that outputs a single number.
Common architecture pattern:
- Input: the prompt concatenated with the candidate response (often with separators).
- Backbone: a transformer encoder or decoder-only transformer (depending on the setup).
- Reward head: a linear layer that maps a pooled representation (often the final token or an aggregate) to one scalar reward.
Practical considerations:
- Length sensitivity: longer answers can look “better” unless the training data and loss discourage verbosity.
- Calibration: raw reward scores are not probabilities; they need careful monitoring to avoid runaway optimisation.
- Separation from the policy: the RM is usually trained separately and then frozen while optimising the policy model.
Training Objective: Turning Preferences into a Learnable Signal
The reward model is commonly trained using a pairwise ranking loss. For each labelled pair (chosen, rejected), the RM learns to give a higher score to the chosen output.
A typical formulation uses a logistic loss (inspired by Bradley–Terry models):
- Let rθ(x,y)r_\theta(x, y)rθ(x,y) be the reward model’s score for prompt xxx and response yyy.
- Minimise a loss that increases when the RM ranks the chosen response above the rejected one.
Training details that matter:
- Batch construction: mixing tasks and difficulty levels reduces overfitting.
- Regularisation: helps resist label noise and prevents extreme reward scaling.
- Validation sets: include both general prompts and safety-focused prompts to detect regressions early.
This is also where many teams discover that “more data” is not enough; you need representative and well-labelled data, which is a key lesson in any generative AI course in Pune covering applied model alignment.
Common Failure Modes and How Teams Reduce Them
Reward modelling introduces its own risks:
- Label noise: humans disagree, especially on nuanced prompts. Mitigation includes clearer rubrics, consensus labelling, and annotator calibration.
- Reward hacking: the policy model may exploit quirks in the RM (for example, producing generic-sounding, overly confident answers). Mitigation includes adversarial data, periodic RM refresh, and adding factuality checks.
- Distribution shift: real users ask new kinds of questions. Mitigation includes continuous data collection, targeted re-labelling, and monitoring reward score drift.
Conclusion
The reward model is the engine that converts human judgement into a training signal: it learns from preference data, applies a transformer-based scoring architecture, and uses pairwise ranking losses to predict what people will prefer. When built carefully—with diverse data sources, strong annotation standards, and safeguards against reward hacking—the reward model becomes a practical bridge between human expectations and model behaviour. For learners exploring RLHF through a generative AI course in Pune, mastering this process is one of the most direct ways to understand how modern AI systems are aligned for real-world use.
