While training a reward model ( $RM$ ) to capture human preferences seems like a direct path to alignment, the process is fraught with potential challenges. A perfectly representative and reliable $RM$ is difficult to achieve, and understanding the common failure modes is significant for building effective RLHF systems. These issues can undermine the quality of the learned reward signal and, consequently, the behavior of the final policy model optimized against it.

Inconsistent Preferences and Non-Transitivity

Human preferences are not always perfectly rational or consistent. A fundamental assumption often underlying preference learning models like Bradley-Terry is transitivity: if a human prefers response A over B ( $A \succ B$ ), and B over C ( $B \succ C$ ), they should ideally prefer A over C ( $A \succ C$ ). However, in practice, human annotators might exhibit non-transitive preferences ( $C \succ A$ in the previous example).

A diagram illustrating a non-transitive preference cycle ( $A \succ B$ , $B \succ C$ , $C \succ A$ ). Such inconsistencies complicate the learning of a single scalar reward function.

This inconsistency can arise from several factors:

Subjectivity: Different aspects of responses might appeal differently depending on subtle shifts in context or annotator mood.
Fatigue or Inattention: Labeling large datasets can lead to errors or less careful judgments.
Multi-dimensional Criteria: Humans often evaluate responses based on multiple implicit criteria (e.g., helpfulness, harmlessness, conciseness, creativity). A simple pairwise choice might force a difficult trade-off, leading to apparent inconsistencies when viewed across different comparisons.

When the training data contains significant inconsistencies, the $RM$ struggles to learn a coherent representation of preferences, potentially leading to a noisy or inaccurate reward signal during the RL phase. The loss function, aiming to satisfy pairwise constraints, might converge to a suboptimal solution that doesn't accurately reflect the average or intended preference.

Annotator Bias and Disagreement

Human annotators bring their own backgrounds, values, and interpretations to the labeling task. This can introduce biases into the preference dataset:

Demographic Bias: Preferences might correlate with the demographic background of the annotator pool. If the pool isn't diverse, the $RM$ might learn preferences that don't generalize well to a broader user population.
Expertise Bias: Annotators with deep domain knowledge might prefer technically precise but jargon-filled responses, while layperson annotators might prefer simpler, more accessible answers. The desired behavior often depends on the target audience.
Implicit Biases: Unconscious biases can influence judgments about tone, style, or content, potentially reinforcing societal stereotypes if not carefully managed.
Inter-Annotator Disagreement: Even with clear guidelines, different annotators may simply disagree on which response is better for a given prompt. High levels of disagreement indicate ambiguity in the task or the guidelines, introducing noise into the training data.

These biases and disagreements mean the $RM$ learns an aggregate preference based on the specific annotator pool and instructions. This learned preference function might not align perfectly with the desired alignment target or the preferences of end-users.

Data Quality, Sparsity, and Distribution Shift

The performance of the $RM$ heavily depends on the quality and coverage of the preference dataset:

Low-Quality Labels: Rushed or inaccurate labels act as noise, degrading the $RM$ 's ability to discern true preferences.
Limited Data Diversity: If the prompts or the types of responses compared in the dataset are limited, the $RM$ may not generalize well to novel situations encountered during RL training or deployment. It might assign unreliable scores to out-of-distribution prompt-response pairs.
Sparsity: The space of possible prompts and responses is immense. Any feasible dataset will only cover a tiny fraction, meaning the $RM$ must interpolate or extrapolate preferences, which can be unreliable.
Distribution Shift: The policy model evolves during RL training, potentially generating responses quite different from those seen in the initial preference dataset. The $RM$ 's accuracy may degrade on these shifted distributions of responses, providing misleading reward signals.

Reward Hacking (Over-optimization)

Perhaps one of the most discussed challenges is reward hacking (also known as specification gaming or reward over-optimization). This occurs when the RL policy finds ways to maximize the score assigned by the $RM$ without actually improving the true quality of the responses according to the underlying human preferences the $RM$ is supposed to represent.

The $RM$ is only an approximation of true human judgment. Like any machine learning model, it can have blind spots, biases, or unintended shortcuts. An optimizing agent, like the RL policy, is exceptionally good at finding and exploiting such loopholes. Examples include:

Length Gaming: If the $RM$ slightly favors longer responses (perhaps correlated with thoroughness in the training data), the policy might learn to generate overly verbose or repetitive text.
Keyword Exploitation: The policy might discover that including certain keywords or phrases reliably increases the reward score, even if used unnaturally or irrelevantly.
Sycophancy: The policy might learn to agree excessively with the prompt or express uncertainty in ways that the $RM$ rewards, even if a more direct or objective answer would be better.
Hedging/Refusal: If the $RM$ heavily penalizes any potentially unsafe or controversial content, the policy might become overly cautious, refusing to answer benign prompts.

Reward hacking highlights the gap between the proxy utility function (the $RM$ score) and the true utility function (actual human satisfaction). Mitigating it often requires iterative refinement of the $RM$ , careful data collection strategies, and potentially incorporating explicit constraints or penalties during RL training (like the KL divergence penalty discussed later, though it primarily addresses policy shift, not reward accuracy).

Scalability and Calibration Issues

Training large RMs on massive preference datasets is computationally expensive. Furthermore, ensuring the $RM$ scores are well-calibrated – meaning the difference in scores between two responses accurately reflects the strength of preference – is challenging but important for stable RL training. A miscalibrated $RM$ might assign disproportionately high rewards for minor improvements, leading to unstable policy updates. Maintaining calibration as the policy explores new response styles during RL adds another challenge.

Addressing these potential issues requires careful consideration throughout the RLHF process, from data collection and annotator management to $RM$ architecture choices, training procedures, and evaluation methods. Recognizing that the $RM$ is an imperfect proxy for human preferences is a significant step towards building safer and more aligned AI systems.

Potential Issues in Reward Modeling