@inproceedings{jia-etal-2026-weakest,
title = "The Weakest Link Tells It All: Outcome-Supervised Process Reward Modeling via Learnable Credit Assignment",
author = "Jia, Tianyu and
Fang, Yue and
Ding, Hongxin and
Qiu, Rihong and
Yang, Zhibang and
Wu, Zhijing and
Zhao, Junfeng and
Wang, Yasha and
Chu, Xu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.486/",
pages = "11160--11192",
ISBN = "979-8-89176-459-0",
abstract = "Process reward models (PRMs) enhance the reasoning capabilities of large language models (LLMs) by providing fine-grained feedback, yet training PRMs typically requires expensive stepwise annotations. Outcome-supervised PRMs offer a scalable alternative by learning from final-answer correctness alone, but this introduces a fundamental *credit assignment* challenge, i.e., attributing outcomes to responsible reasoning steps. Existing approaches rely on either uniform or causal assignment, both of which fail to anchor credit in step correctness and thus hinder process error identification.\par In this work, we propose Outcome-Supervised Process Reward Modeling via **L**earnable **C**redit **A**ssignment (**LCA**), an outcome-supervised PRM framework that jointly learns credit assignment and reward modeling under the principle of *Weakest Link Assignment: a reasoning chain is as strong as its weakest link*. To address mutual dependence between credit assignment and reward modeling, we formalize outcome-supervised PRM as a Multiple Instance Learning (MIL) problem and introduce Softmax-Weighted-Sum (SWS) pooling, an MIL pooling technique tailored for strong dependence and redundancy among reasoning states. We establish instance-level Bayes consistency of the resulting learning objective under mild assumptions. Extensive experiments demonstrate that **LCA** outperforms state-of-the-art outcome-supervised PRMs across multiple tasks and backbones. Code is available at https://github.com/JiaTianyu20031016/MIL.git."
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<abstract>Process reward models (PRMs) enhance the reasoning capabilities of large language models (LLMs) by providing fine-grained feedback, yet training PRMs typically requires expensive stepwise annotations. Outcome-supervised PRMs offer a scalable alternative by learning from final-answer correctness alone, but this introduces a fundamental *credit assignment* challenge, i.e., attributing outcomes to responsible reasoning steps. Existing approaches rely on either uniform or causal assignment, both of which fail to anchor credit in step correctness and thus hinder process error identification.\par In this work, we propose Outcome-Supervised Process Reward Modeling via **L**earnable **C**redit **A**ssignment (**LCA**), an outcome-supervised PRM framework that jointly learns credit assignment and reward modeling under the principle of *Weakest Link Assignment: a reasoning chain is as strong as its weakest link*. To address mutual dependence between credit assignment and reward modeling, we formalize outcome-supervised PRM as a Multiple Instance Learning (MIL) problem and introduce Softmax-Weighted-Sum (SWS) pooling, an MIL pooling technique tailored for strong dependence and redundancy among reasoning states. We establish instance-level Bayes consistency of the resulting learning objective under mild assumptions. Extensive experiments demonstrate that **LCA** outperforms state-of-the-art outcome-supervised PRMs across multiple tasks and backbones. Code is available at https://github.com/JiaTianyu20031016/MIL.git.</abstract>
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%0 Conference Proceedings
%T The Weakest Link Tells It All: Outcome-Supervised Process Reward Modeling via Learnable Credit Assignment
%A Jia, Tianyu
%A Fang, Yue
%A Ding, Hongxin
%A Qiu, Rihong
%A Yang, Zhibang
%A Wu, Zhijing
%A Zhao, Junfeng
%A Wang, Yasha
%A Chu, Xu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F jia-etal-2026-weakest
%X Process reward models (PRMs) enhance the reasoning capabilities of large language models (LLMs) by providing fine-grained feedback, yet training PRMs typically requires expensive stepwise annotations. Outcome-supervised PRMs offer a scalable alternative by learning from final-answer correctness alone, but this introduces a fundamental *credit assignment* challenge, i.e., attributing outcomes to responsible reasoning steps. Existing approaches rely on either uniform or causal assignment, both of which fail to anchor credit in step correctness and thus hinder process error identification.\par In this work, we propose Outcome-Supervised Process Reward Modeling via **L**earnable **C**redit **A**ssignment (**LCA**), an outcome-supervised PRM framework that jointly learns credit assignment and reward modeling under the principle of *Weakest Link Assignment: a reasoning chain is as strong as its weakest link*. To address mutual dependence between credit assignment and reward modeling, we formalize outcome-supervised PRM as a Multiple Instance Learning (MIL) problem and introduce Softmax-Weighted-Sum (SWS) pooling, an MIL pooling technique tailored for strong dependence and redundancy among reasoning states. We establish instance-level Bayes consistency of the resulting learning objective under mild assumptions. Extensive experiments demonstrate that **LCA** outperforms state-of-the-art outcome-supervised PRMs across multiple tasks and backbones. Code is available at https://github.com/JiaTianyu20031016/MIL.git.
%U https://aclanthology.org/2026.emnlp-main.486/
%P 11160-11192
Markdown (Informal)
[The Weakest Link Tells It All: Outcome-Supervised Process Reward Modeling via Learnable Credit Assignment](https://aclanthology.org/2026.emnlp-main.486/) (Jia et al., EMNLP 2026)
ACL
- Tianyu Jia, Yue Fang, Hongxin Ding, Rihong Qiu, Zhibang Yang, Zhijing Wu, Junfeng Zhao, Yasha Wang, and Xu Chu. 2026. The Weakest Link Tells It All: Outcome-Supervised Process Reward Modeling via Learnable Credit Assignment. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 11160–11192, Budapest, Hungary. Association for Computational Linguistics.