@inproceedings{zhang-etal-2025-lets,
title = "{L}e{TS}: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization",
author = "Zhang, Qi and
Yang, Shouqing and
Gao, Lirong and
Chen, Hao and
Hu, Xiaomeng and
Chen, Jinglei and
Wang, Jiexiang and
Guo, Sheng and
Zheng, Bo and
Wang, Haobo and
Zhao, Junbo",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.257/",
pages = "5109--5122",
ISBN = "979-8-89176-332-6",
abstract = "Large language models (LLMs) have demonstrated impressive capabilities in reasoning with the emergence of reasoning models like OpenAI-o1 and DeepSeek-R1. Recent research focuses on integrating reasoning capabilities into the realm of retrieval-augmented generation (RAG) via outcome-supervised reinforcement learning (RL) approaches, while the correctness of intermediate think-and-search steps is usually neglected. To address this issue, we design a process-level reward module to mitigate the unawareness of intermediate reasoning steps in outcome-level supervision without additional annotation. Grounded on this, we propose **Le**arning to **T**hink-and-**S**earch (**LeTS**), a novel framework that hybridizes stepwise process reward and outcome-based reward to current RL methods for RAG. Extensive experiments demonstrate the generalization and inference efficiency of **LeTS** across various RAG benchmarks. In addition, these results reveal the potential of process- and outcome-level reward hybridization in boosting LLMs' reasoning ability via RL under other scenarios."
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<abstract>Large language models (LLMs) have demonstrated impressive capabilities in reasoning with the emergence of reasoning models like OpenAI-o1 and DeepSeek-R1. Recent research focuses on integrating reasoning capabilities into the realm of retrieval-augmented generation (RAG) via outcome-supervised reinforcement learning (RL) approaches, while the correctness of intermediate think-and-search steps is usually neglected. To address this issue, we design a process-level reward module to mitigate the unawareness of intermediate reasoning steps in outcome-level supervision without additional annotation. Grounded on this, we propose **Le**arning to **T**hink-and-**S**earch (**LeTS**), a novel framework that hybridizes stepwise process reward and outcome-based reward to current RL methods for RAG. Extensive experiments demonstrate the generalization and inference efficiency of **LeTS** across various RAG benchmarks. In addition, these results reveal the potential of process- and outcome-level reward hybridization in boosting LLMs’ reasoning ability via RL under other scenarios.</abstract>
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%0 Conference Proceedings
%T LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization
%A Zhang, Qi
%A Yang, Shouqing
%A Gao, Lirong
%A Chen, Hao
%A Hu, Xiaomeng
%A Chen, Jinglei
%A Wang, Jiexiang
%A Guo, Sheng
%A Zheng, Bo
%A Wang, Haobo
%A Zhao, Junbo
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F zhang-etal-2025-lets
%X Large language models (LLMs) have demonstrated impressive capabilities in reasoning with the emergence of reasoning models like OpenAI-o1 and DeepSeek-R1. Recent research focuses on integrating reasoning capabilities into the realm of retrieval-augmented generation (RAG) via outcome-supervised reinforcement learning (RL) approaches, while the correctness of intermediate think-and-search steps is usually neglected. To address this issue, we design a process-level reward module to mitigate the unawareness of intermediate reasoning steps in outcome-level supervision without additional annotation. Grounded on this, we propose **Le**arning to **T**hink-and-**S**earch (**LeTS**), a novel framework that hybridizes stepwise process reward and outcome-based reward to current RL methods for RAG. Extensive experiments demonstrate the generalization and inference efficiency of **LeTS** across various RAG benchmarks. In addition, these results reveal the potential of process- and outcome-level reward hybridization in boosting LLMs’ reasoning ability via RL under other scenarios.
%U https://aclanthology.org/2025.emnlp-main.257/
%P 5109-5122
Markdown (Informal)
[LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization](https://aclanthology.org/2025.emnlp-main.257/) (Zhang et al., EMNLP 2025)
ACL
- Qi Zhang, Shouqing Yang, Lirong Gao, Hao Chen, Xiaomeng Hu, Jinglei Chen, Jiexiang Wang, Sheng Guo, Bo Zheng, Haobo Wang, and Junbo Zhao. 2025. LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 5109–5122, Suzhou, China. Association for Computational Linguistics.