From Soft Targets to Reward Signals: How Assignment and Reward Objectives Interact

2026-09-28

AI Summary

This study investigates how the interaction between preference objective allocation and reward functions influences signal generation. Methodologically, it introduces an assignment geometry to analyze their relationship and constructs a joint design space to reveal the synergistic mechanisms underlying objective placement and reward shaping. The evaluation pipeline is further optimized through techniques including mean-matching smoothing, intra-layer reallocation, and APLOT-based objective unification. The findings elucidate reward characteristics under varying objective distributions, demonstrating that complete correspondence preserves the maximum preference margin and that the decay order dynamically shifts with the reward function. Overall, this work provides theoretical grounding for understanding the coupled effects of objective allocation and reward design in preference optimization.

Abstract

Soft preference targets specify supervision strength, and reward objectives convert that strength into learned reward signals. A central design question remains: how does assigning a fixed set of preference strengths to different response pairs change the rewards produced by different objectives? We introduce assignment geometry to study this interaction. Mean-matched smoothing controls target dispersion, while within-stratum reassignment changes correspondence and preserves the complete target distribution. Across five reward objectives, intact correspondence retains the largest clean preference margins among the compared soft targets within a common accuracy-equivalence budget. Attenuation orderings change with the reward objective, revealing different responses to the same target assignments. Independent reassignments and a related source construction reproduce the retention direction. An attenuation-retention profile compares these combinations through margin magnitude, edit response, and accuracy. Against independently calibrated scaling, APLOT uniform targets deliver additional attenuation on both aggregate and presentation edits. These findings establish a joint design space in which target placement and reward objective shape reward properties beyond preference accuracy.

Problem

• preference learning
• reward objectives
• soft targets
• assignment geometry
• reward signals

Innovation

• assignment geometry
• soft preference targets
• reward objectives
• attenuation-retention profile
• APLOT