Reinforcement Learning from Human Feedback (RLHF)

The core mechanism of RLHF where a model is trained to maximize a reward signal derived from human pairwise comparisons. Instead of requiring absolute scores, annotators simply indicate which of two generated outputs they prefer. Direct Preference Optimization (DPO) has emerged as a streamlined alternative that bypasses the need for an explicit reward model by directly optimizing the policy from preference data, reducing computational overhead while maintaining alignment quality.

A supervised learning task that trains a scalar reward model to predict human preference scores. The reward model ingests prompt-response pairs and outputs a single numerical value representing quality. In clinical contexts, this model learns to penalize hallucinated medical facts, missed critical findings, and non-compliant formatting while rewarding accurate entity extraction and proper negation handling. The reward signal serves as the training objective for proximal policy optimization.

The reinforcement learning algorithm most commonly paired with reward models in RLHF pipelines. PPO updates the language model's policy while constraining the magnitude of each update to prevent catastrophic forgetting of pre-trained knowledge. A KL divergence penalty term ensures the aligned model does not drift too far from its base distribution, preserving general capabilities while refining behavior according to human feedback.

An alternative alignment paradigm developed by Anthropic that reduces reliance on human feedback by using a set of written principles—a constitution—to guide model behavior. The model generates self-critiques and revisions based on these rules, then trains on the refined outputs. This approach is particularly relevant for clinical applications where explicit safety policies can be codified, such as:

• Never fabricate patient data
• Always flag uncertainty
• Preserve PHI redaction boundaries

The annotation phase that generates the training signal for RLHF. Clinical reviewers compare model outputs and select preferred responses based on criteria like factual accuracy, completeness, and adherence to guidelines. Key considerations include:

• Inter-annotator agreement to ensure label quality
• Calibration sessions to align reviewer standards
• Error taxonomy tagging to capture failure modes This data pipeline directly determines the reward model's ability to generalize to novel clinical scenarios.

The observed degradation in a model's general capabilities—such as reasoning depth, coding proficiency, or creative generation—that can occur as a side effect of RLHF fine-tuning. The alignment process may cause the model to become overly cautious, refusing benign requests or producing bland outputs. Mitigation strategies include constrained optimization, multi-objective reward modeling, and iterative refinement that balances helpfulness, harmlessness, and clinical accuracy without sacrificing performance.