@inproceedings{zhang-etal-2025-reward,
title = "Reward Mixology: Crafting Hybrid Signals for Reinforcement Learning Driven In-Context Learning",
author = "Zhang, Changshuo and
Gao, Ang and
Zhang, Xiao and
Liu, Yong and
Li, Deyang and
Liu, Fangchao and
Zhang, Xinyu",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.234/",
doi = "10.18653/v1/2025.findings-emnlp.234",
pages = "4373--4383",
ISBN = "979-8-89176-335-7",
abstract = "In-context learning (ICL) performance heavily relies on the quality and ordering of demonstrations. Iterative selection (IS) is a promising approach to address this issue, but existing IS methods face two key challenges: the oversimplification of process reward signals that guide intermediate steps (often using single-dimensional metrics) and the lack of outcome reward signals that directly optimize final-task accuracy (relying solely on binary terminal feedback like correct/incorrect predictions). To address these issues, we propose a reinforcement learning method R-Mix which models iterative demonstration selection as a Markov Decision Process (MDP), crafting hybrid reward signals {---} combining outcome-based accuracy signals (i.e., outcome rewards) with process-oriented signals (i.e, process rewards) like stepwise influence and label entropy improvement. Our analysis reveals a positive but trade-off relationship between outcome rewards and process rewards, underscoring the importance of both components for effective policy optimization. We further introduce a dual-head policy architecture that explicitly decouples input-semantic relevance and label-content compatibility. Experiments across NLP benchmarks demonstrate superior performance over state-of-the-art methods, with ablation studies validating the necessity of both reward components and architectural disentanglement. Our work has deeply explored the effective potential of ICL through demonstration selection."
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<abstract>In-context learning (ICL) performance heavily relies on the quality and ordering of demonstrations. Iterative selection (IS) is a promising approach to address this issue, but existing IS methods face two key challenges: the oversimplification of process reward signals that guide intermediate steps (often using single-dimensional metrics) and the lack of outcome reward signals that directly optimize final-task accuracy (relying solely on binary terminal feedback like correct/incorrect predictions). To address these issues, we propose a reinforcement learning method R-Mix which models iterative demonstration selection as a Markov Decision Process (MDP), crafting hybrid reward signals — combining outcome-based accuracy signals (i.e., outcome rewards) with process-oriented signals (i.e, process rewards) like stepwise influence and label entropy improvement. Our analysis reveals a positive but trade-off relationship between outcome rewards and process rewards, underscoring the importance of both components for effective policy optimization. We further introduce a dual-head policy architecture that explicitly decouples input-semantic relevance and label-content compatibility. Experiments across NLP benchmarks demonstrate superior performance over state-of-the-art methods, with ablation studies validating the necessity of both reward components and architectural disentanglement. Our work has deeply explored the effective potential of ICL through demonstration selection.</abstract>
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%0 Conference Proceedings
%T Reward Mixology: Crafting Hybrid Signals for Reinforcement Learning Driven In-Context Learning
%A Zhang, Changshuo
%A Gao, Ang
%A Zhang, Xiao
%A Liu, Yong
%A Li, Deyang
%A Liu, Fangchao
%A Zhang, Xinyu
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F zhang-etal-2025-reward
%X In-context learning (ICL) performance heavily relies on the quality and ordering of demonstrations. Iterative selection (IS) is a promising approach to address this issue, but existing IS methods face two key challenges: the oversimplification of process reward signals that guide intermediate steps (often using single-dimensional metrics) and the lack of outcome reward signals that directly optimize final-task accuracy (relying solely on binary terminal feedback like correct/incorrect predictions). To address these issues, we propose a reinforcement learning method R-Mix which models iterative demonstration selection as a Markov Decision Process (MDP), crafting hybrid reward signals — combining outcome-based accuracy signals (i.e., outcome rewards) with process-oriented signals (i.e, process rewards) like stepwise influence and label entropy improvement. Our analysis reveals a positive but trade-off relationship between outcome rewards and process rewards, underscoring the importance of both components for effective policy optimization. We further introduce a dual-head policy architecture that explicitly decouples input-semantic relevance and label-content compatibility. Experiments across NLP benchmarks demonstrate superior performance over state-of-the-art methods, with ablation studies validating the necessity of both reward components and architectural disentanglement. Our work has deeply explored the effective potential of ICL through demonstration selection.
%R 10.18653/v1/2025.findings-emnlp.234
%U https://aclanthology.org/2025.findings-emnlp.234/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.234
%P 4373-4383
Markdown (Informal)
[Reward Mixology: Crafting Hybrid Signals for Reinforcement Learning Driven In-Context Learning](https://aclanthology.org/2025.findings-emnlp.234/) (Zhang et al., Findings 2025)
ACL