dorsal/arxiv
View SchemaMAESTRO: Meta-learning Adaptive Estimation of Scalarization Trade-offs for Reward Optimization
| Authors | Yang Zhao, Hepeng Wang, Xiao Ding, Yangou Ouyang, Bibo Cai, Kai Xiong, Jinglong Gao, Zhouhao Sun, Li Du, Bing Qin, Ting Liu |
|---|---|
| Categories | |
| ArXiv ID | 2601.07208vv1 |
| URL | https://arxiv.org/abs/2601.07208 |
| License | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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 (Meta-learning Adaptive Estimation of Scalarization Trade-offs for Reward Optimization), 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.
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"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 (Meta-learning Adaptive Estimation of Scalarization Trade-offs for Reward Optimization), which introduces a meta-cognitive orchestration layer that treats reward scalarization as a dynamic latent policy, leveraging the model\u0027s 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.",
"arxiv_id": "2601.07208",
"authors": [
"Yang Zhao",
"Hepeng Wang",
"Xiao Ding",
"Yangou Ouyang",
"Bibo Cai",
"Kai Xiong",
"Jinglong Gao",
"Zhouhao Sun",
"Li Du",
"Bing Qin",
"Ting Liu"
],
"categories": [
"cs.LG",
"cs.CL"
],
"license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
"title": "MAESTRO: Meta-learning Adaptive Estimation of Scalarization Trade-offs for Reward Optimization",
"url": "https://arxiv.org/abs/2601.07208",
"version": "v1"
},
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