MAESTRO: Meta-learning Adaptive Estimation of Scalarization Trade-offs for Reward Optimization

ACL ARR 2026 January Submission3883 Authors

Keywords: Large Language Models, Reinforcement Learning, Group-Relative Policy Optimization (GRPO), Multi-Objective Optimization, Meta-learning, Reward Scalarization, Open-Domain Alignment

Abstract: Group-Relative Policy Optimization (GRPO) has emerged as an efficient paradigm for aligning Large Language Models (LLMs), yet its efficacy is primarily confined to domains with verifiable ground truths. Extending GRPO to **open-domain settings** remains a critical challenge, as **unconstrained generation** entails multi-faceted and often conflicting objectives—such as creativity versus factuality—where rigid, static reward scalarization is inherently suboptimal. To address this, we propose **MAESTRO** (**M**eta-learning **A**daptive **E**stimation of **S**calarization **T**rade-offs for **R**eward O**ptimization), which introduces a meta-cognitive orchestration layer that treats reward scalarization as a dynamic latent policy, leveraging the model's terminal hidden states as a semantic bottleneck to perceive task-specific priorities. We formulate this as a contextual bandit problem within a bi-level optimization framework, where a lightweight Conductor network co-evolves with the policy by utilizing group-relative advantages as a meta-reward signal. Across seven benchmarks, MAESTRO consistently outperforms single-reward and static multi-objective baselines, while preserving the efficiency advantages of GRPO, and in some settings even reducing redundant generation.

Paper Type: Long

Research Area: Machine Learning for NLP

Research Area Keywords: reinforcement learning,meta learning

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Publicly available software and/or pre-trained models, Theory

Languages Studied: English

Submission Number: 3883