@inproceedings{ma-etal-2026-tspo,
title = "{TSPO}: Breaking the Double Homogenization Dilemma in Multi-turn Search Policy Optimization",
author = "Ma, Shichao and
Ma, Zhiyuan and
Yang, Ming and
Li, Xiaofan and
Wu, Xing and
Du, Jintao and
Cheng, Yu and
Wang, Weiqiang and
Liu, Qiliang and
Zhou, Zhengyang and
Wang, Yang",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1428/",
pages = "28607--28623",
ISBN = "979-8-89176-395-1",
abstract = "Multi-turn tool-integrated reasoning enables Large Language Models (LLMs) to solve complex tasks through iterative information retrieval. However, current reinforcement learning (RL) frameworks for search-augmented reasoning predominantly rely on sparse outcome-level rewards, leading to a ``Double Homogenization Dilemma.'' This manifests as (1) Process homogenization, where the thinking, reasoning, and tooling involved in generation are ignored. (2) Intra-group homogenization, coarse-grained outcome rewards often lead to inefficiencies in intra-group advantage estimation with methods like Group Relative Policy Optimization (GRPO) during sampling. To address this, we propose Turn-level Stage-aware Policy Optimization (TSPO). TSPO introduces the First-Occurrence Latent Reward (FOLR) mechanism, allocating partial rewards to the step where the ground-truth answer first appears, thereby preserving process-level signals and increasing reward variance within groups without requiring external reward models or any annotations. Extensive experiments demonstrate that TSPO significantly outperforms state-of-the-art baselines, achieving average performance gains of 24{\%} and 13.6{\%} on Qwen2.5-3B and 7B models, respectively. Code is available at https://github.com/Flipped-May/TSPO."
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<abstract>Multi-turn tool-integrated reasoning enables Large Language Models (LLMs) to solve complex tasks through iterative information retrieval. However, current reinforcement learning (RL) frameworks for search-augmented reasoning predominantly rely on sparse outcome-level rewards, leading to a “Double Homogenization Dilemma.” This manifests as (1) Process homogenization, where the thinking, reasoning, and tooling involved in generation are ignored. (2) Intra-group homogenization, coarse-grained outcome rewards often lead to inefficiencies in intra-group advantage estimation with methods like Group Relative Policy Optimization (GRPO) during sampling. To address this, we propose Turn-level Stage-aware Policy Optimization (TSPO). TSPO introduces the First-Occurrence Latent Reward (FOLR) mechanism, allocating partial rewards to the step where the ground-truth answer first appears, thereby preserving process-level signals and increasing reward variance within groups without requiring external reward models or any annotations. Extensive experiments demonstrate that TSPO significantly outperforms state-of-the-art baselines, achieving average performance gains of 24% and 13.6% on Qwen2.5-3B and 7B models, respectively. Code is available at https://github.com/Flipped-May/TSPO.</abstract>
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%0 Conference Proceedings
%T TSPO: Breaking the Double Homogenization Dilemma in Multi-turn Search Policy Optimization
%A Ma, Shichao
%A Ma, Zhiyuan
%A Yang, Ming
%A Li, Xiaofan
%A Wu, Xing
%A Du, Jintao
%A Cheng, Yu
%A Wang, Weiqiang
%A Liu, Qiliang
%A Zhou, Zhengyang
%A Wang, Yang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F ma-etal-2026-tspo
%X Multi-turn tool-integrated reasoning enables Large Language Models (LLMs) to solve complex tasks through iterative information retrieval. However, current reinforcement learning (RL) frameworks for search-augmented reasoning predominantly rely on sparse outcome-level rewards, leading to a “Double Homogenization Dilemma.” This manifests as (1) Process homogenization, where the thinking, reasoning, and tooling involved in generation are ignored. (2) Intra-group homogenization, coarse-grained outcome rewards often lead to inefficiencies in intra-group advantage estimation with methods like Group Relative Policy Optimization (GRPO) during sampling. To address this, we propose Turn-level Stage-aware Policy Optimization (TSPO). TSPO introduces the First-Occurrence Latent Reward (FOLR) mechanism, allocating partial rewards to the step where the ground-truth answer first appears, thereby preserving process-level signals and increasing reward variance within groups without requiring external reward models or any annotations. Extensive experiments demonstrate that TSPO significantly outperforms state-of-the-art baselines, achieving average performance gains of 24% and 13.6% on Qwen2.5-3B and 7B models, respectively. Code is available at https://github.com/Flipped-May/TSPO.
%U https://aclanthology.org/2026.findings-acl.1428/
%P 28607-28623
Markdown (Informal)
[TSPO: Breaking the Double Homogenization Dilemma in Multi-turn Search Policy Optimization](https://aclanthology.org/2026.findings-acl.1428/) (Ma et al., Findings 2026)
ACL
- Shichao Ma, Zhiyuan Ma, Ming Yang, Xiaofan Li, Xing Wu, Jintao Du, Yu Cheng, Weiqiang Wang, Qiliang Liu, Zhengyang Zhou, and Yang Wang. 2026. TSPO: Breaking the Double Homogenization Dilemma in Multi-turn Search Policy Optimization. In Findings of the Association for Computational Linguistics: ACL 2026, pages 28607–28623, San Diego, California, United States. Association for Computational Linguistics.