AMoPO uses the model's own token probabilities to define Gaussian-sampled weights, combining per-dimension SimPO-style losses for reference-free multi-objective alignment.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
AMoPO: Adaptive Multi-objective Preference Optimization without Reward Models and Reference Models
AMoPO uses the model's own token probabilities to define Gaussian-sampled weights, combining per-dimension SimPO-style losses for reference-free multi-objective alignment.