While standard Reinforcement Learning from Human Feedback (RLHF) typically optimizes a language model against a single, unified reward signal derived from human preferences, real-world alignment often involves balancing multiple, sometimes competing, objectives. A model should ideally be helpful, harmless, and honest, but maximizing one of these might negatively impact others. For instance, maximizing helpfulness without constraint could lead to generating harmful content if requested. Multi-Objective Reward Models (MORMs) provide a framework for addressing this challenge by explicitly modeling and optimizing for several criteria simultaneously.

Limitations of Single-Objective Rewards

Training a single reward model (RM) based on overall preference (e.g., "Which response is better?") implicitly averages over various underlying factors that contribute to that preference. This can obscure important trade-offs. An annotator might prefer response A because it's slightly more helpful, even if response B is significantly safer. A single reward score might not adequately capture this multi-faceted evaluation, potentially leading the RL policy to over-optimize for one aspect at the expense of others.

Defining and Modeling Multiple Objectives

Instead of learning a single reward function $R(prompt, response)$ , the multi-objective approach aims to learn multiple reward functions, each corresponding to a specific alignment criterion. For example, we might define:

$R_{helpful}(p, y)$ : Reward for helpfulness.
$R_{harmless}(p, y)$ : Reward for harmlessness.
$R_{honest}(p, y)$ : Reward for honesty.

These objectives often stem from principles defined during the project's design, such as those outlined in Anthropic's Constitutional AI work (Helpful, Harmless, Honest - HHH).

There are two main ways to implement this:

Separate Reward Models: Train distinct RM models for each objective ( $RM_{helpful}$ , $RM_{harmless}$ , etc.). Each model is trained on preference data specifically labeled for that objective. This requires more granular annotation, where humans might rate or compare responses along each dimension separately.
Multi-Headed Reward Model: Train a single neural network that shares a common base (e.g., processing the prompt and response) but has multiple output "heads," one for each objective. This can be more parameter-efficient and allows the model to potentially learn shared representations relevant to multiple objectives. The output would be a vector of rewards: $[R_{helpful}, R_{harmless}, R_{honest}]$ .

Adapting Preference Data Collection

Collecting data for MORMs necessitates a more detailed annotation process compared to single-objective pairwise comparisons. Annotators might be asked to:

Provide ratings on scales for each objective (e.g., rate helpfulness 1-5, harmlessness 1-5).
Perform pairwise comparisons specific to each objective (e.g., "Which response is more harmless?").
Choose the preferred response overall but also provide reasons related to specific objectives.

The richness and granularity of this data directly impact the quality of the resulting multi-objective reward signals.

Training Multi-Objective Reward Models

If using separate models, each is trained independently using standard RM training techniques (like the Bradley-Terry model) on its specific preference data subset.

For a multi-headed model, the training process needs to handle multiple outputs. The loss function is typically a sum or weighted sum of the individual loss terms for each objective head. For instance, if using a pairwise preference loss $L_{pref}$ for each objective, the total loss might be:

L_{total} = w_{helpful} L_{pref, helpful} + w_{harmless} L_{pref, harmless} + w_{honest} L_{pref, honest}

Here, $w_{helpful}, w_{harmless}, w_{honest}$ are weights that control the relative importance of fitting each objective during RM training. These weights are hyperparameters that need careful tuning.

Integrating Multi-Objective Rewards into RL Fine-Tuning

Once you have reward values for each objective (either from separate models or a multi-headed one), you need to combine them into a single scalar reward signal that the RL algorithm (like PPO) can use to update the policy. The most common method is scalarization, typically through a weighted sum:

R_{combined}(p, y) = w'_{helpful} R_{helpful}(p, y) + w'_{harmless} R_{harmless}(p, y) + w'_{honest} R_{honest}(p, y)

The weights ( $w'_{helpful}, w'_{harmless}, w'_{honest}$ ) used here during the RL phase are critical hyperparameters. They directly control the trade-offs the policy learns to make between the different objectives. For example, assigning a very high weight $w'_{harmless}$ will strongly incentivize the policy to avoid generating potentially harmful content, possibly even at the cost of reduced helpfulness for borderline queries.

These weights might be static throughout training or dynamically adjusted. Choosing appropriate weights is often an iterative process involving evaluation and analysis of the resulting model's behavior.

Example scalarization weights used during RL fine-tuning, emphasizing harmlessness slightly more than helpfulness, with less weight on honesty in this configuration.

Challenges and Considerations

Objective Definition: Clearly defining and measuring objectives like "harmlessness" or "honesty" is inherently difficult and can be subjective.
Data Cost: Collecting granular, multi-objective preference data is typically more expensive and time-consuming than collecting simple pairwise preferences.
Weight Tuning: Finding the right weights for both RM training (if multi-headed) and RL scalarization ( $w$ and $w'$ ) requires careful experimentation and evaluation, as different weightings lead to different model behaviors and trade-offs.
Objective Conflicts: Objectives can inherently conflict. For example, being maximally honest might sometimes require providing information that could be misused (conflicting with harmlessness). The scalarization approach forces a specific trade-off based on the chosen weights. More advanced multi-objective optimization techniques exist (e.g., finding Pareto optimal solutions) but are less common in current large-scale RLHF due to complexity.
Reward Gaming: Just like single-objective RMs, MORMs can be susceptible to reward hacking, potentially focused on the specific objectives being measured.

By explicitly modeling multiple alignment criteria, MORMs offer a more controllable way to navigate the complex trade-offs involved in aligning LLMs with human values, moving beyond a single notion of "better" towards a more structured understanding of desirable model behavior.