STAGE: Controlled Objective Admission for Multi-Preference LLM Alignment

Abstract:Multi-preference alignment is often framed as scalarization: combine reward dimensions, then optimize. This leaves a temporal decision underspecified: when should each preference dimension enter policy optimization? We propose \methodname, a stability-guided active-set controller for controlled objective admission. \methodname starts from a small active set, retains admitted objectives, and expands when reward-deviation gates indicate low recent deviation or a patience budget is exhausted. A probing phase estimates a hard-to-easy order, and adaptive weighting emphasizes underperforming active dimensions. Automatic evaluations with 15 training preferences and 16 held-out benchmark columns show that \methodname obtains higher averages than simultaneous scalarization and shared-budget adapted baselines. Component ablations and expansion dynamics further support cumulative retention, gated admission, and probing-derived ordering as useful design choices in this setting. These results position objective-entry timing as a concrete control variable in reward-vector RLHF.

Computation and Language (cs.CL)

arXiv:2608.16553 [cs.CL]

(or arXiv:2608.16553v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.16553 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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