A Value Vector is a structured numerical representation that encodes the relative importance or utility of different outcomes, objectives, or features within an AI system, enabling multi-objective optimisation and preference-guided decision-making. It aggregates scalar values across dimensions such as safety, performance, cost, and alignment to express composite desiderata, and is foundational to reward modelling, preference learning, and value alignment research.
A Value Vector is a structured numerical representation that encodes the relative importance or utility of different outcomes, objectives, or features within an AI system. It aggregates scalar values across multiple dimensions—such as safety, performance, fairness, and cost—to express composite desiderata, enabling preference-guided decision-making and multi-objective optimisation. Value vectors are foundational to reward modelling, preference learning, and value alignment research, where the goal is to represent human values in a machine-tractable form.
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Value vectors formalise the intuition that real-world AI systems must balance competing objectives rather than optimise a single scalar metric. In reinforcement learning, the reward signal is typically a scalar, but a value vector extends this to a multi-dimensional tuple where each component represents a distinct desideratum such as task performance, energy efficiency, safety violations, or user satisfaction. The agent or training procedure then applies a scalarisation function—such as a weighted sum or Chebyshev aggregation—to collapse the vector into a single value for gradient computation while preserving the ability to inspect and adjust individual components.
In preference learning and reinforcement learning from human feedback (RLHF), value vectors provide a structured way to decompose human judgements into interpretable dimensions, making the reward model more transparent and auditable. A trained reward model can output a vector of sub-scores, each representing a human-legible criterion, before aggregating into the final training signal. This separation supports alignment research by allowing practitioners to detect when a model is optimising one dimension at the expense of another.
Value vectors are also relevant to multi-objective optimisation and evolutionary algorithms, where a population of solutions is evaluated along a Pareto frontier defined by the vector’s components. This enables trade-off analysis and supports stakeholder decision-making by visualising which solutions dominate across all objectives and which represent acceptable compromises.
Implementation considerations include the choice of aggregation function, the calibration of individual dimension weights, and the handling of incommensurable units across components. Poorly calibrated value vectors can lead to reward hacking, where a model achieves a high aggregate score by gaming a poorly weighted component, underscoring the importance of rigorous vector design and ongoing monitoring.