@inproceedings{zhang-etal-2026-treeps,
title = "{T}ree{PS}-{RAG}: Tree-based Process Supervision for Reinforcement Learning in Agentic {RAG}",
author = "Zhang, Tianhua and
Li, Kun and
Li, Junan and
Li, Yunxiang and
Luo, Hongyin and
Wu, Xixin and
Glass, James R. and
Meng, Helen M.",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1007/",
pages = "21297--21316",
ISBN = "979-8-89176-462-0",
abstract = "Agentic retrieval-augmented generation (RAG) formulates question answering as a multi-step interaction between reasoning and information retrieval, and has recently been advanced by reinforcement learning (RL) with outcome-based supervision. While effective, relying solely on sparse final rewards limits step-wise credit assignment and provides weak guidance for intermediate reasoning and actions. Recent efforts explore process-level supervision, but typically depend on offline constructed training data, which risks distribution shift, or require costly intermediate annotations. We present **TreePS-RAG**, an online, tree-based RL framework for agentic RAG that enables step-wise credit assignment while retaining standard outcome-only rewards. Our key insight is to model agentic RAG reasoning as a rollout tree, where each reasoning step naturally maps to a node. This tree structure allows step utility to be estimated via Monte Carlo estimation over its descendant outcomes, yielding fine-grained process advantages without requiring intermediate labels. To make this paradigm practical, we introduce an efficient online tree construction strategy that preserves exploration diversity under a constrained computational budget. With a rollout cost comparable to strong baselines like Search-R1, experiments on seven multi-hop and general QA benchmarks across multiple model scales show that TreePS-RAG consistently outperforms both outcome-supervised and leading process-supervised RL methods."
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<abstract>Agentic retrieval-augmented generation (RAG) formulates question answering as a multi-step interaction between reasoning and information retrieval, and has recently been advanced by reinforcement learning (RL) with outcome-based supervision. While effective, relying solely on sparse final rewards limits step-wise credit assignment and provides weak guidance for intermediate reasoning and actions. Recent efforts explore process-level supervision, but typically depend on offline constructed training data, which risks distribution shift, or require costly intermediate annotations. We present **TreePS-RAG**, an online, tree-based RL framework for agentic RAG that enables step-wise credit assignment while retaining standard outcome-only rewards. Our key insight is to model agentic RAG reasoning as a rollout tree, where each reasoning step naturally maps to a node. This tree structure allows step utility to be estimated via Monte Carlo estimation over its descendant outcomes, yielding fine-grained process advantages without requiring intermediate labels. To make this paradigm practical, we introduce an efficient online tree construction strategy that preserves exploration diversity under a constrained computational budget. With a rollout cost comparable to strong baselines like Search-R1, experiments on seven multi-hop and general QA benchmarks across multiple model scales show that TreePS-RAG consistently outperforms both outcome-supervised and leading process-supervised RL methods.</abstract>
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%0 Conference Proceedings
%T TreePS-RAG: Tree-based Process Supervision for Reinforcement Learning in Agentic RAG
%A Zhang, Tianhua
%A Li, Kun
%A Li, Junan
%A Li, Yunxiang
%A Luo, Hongyin
%A Wu, Xixin
%A Glass, James R.
%A Meng, Helen M.
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zhang-etal-2026-treeps
%X Agentic retrieval-augmented generation (RAG) formulates question answering as a multi-step interaction between reasoning and information retrieval, and has recently been advanced by reinforcement learning (RL) with outcome-based supervision. While effective, relying solely on sparse final rewards limits step-wise credit assignment and provides weak guidance for intermediate reasoning and actions. Recent efforts explore process-level supervision, but typically depend on offline constructed training data, which risks distribution shift, or require costly intermediate annotations. We present **TreePS-RAG**, an online, tree-based RL framework for agentic RAG that enables step-wise credit assignment while retaining standard outcome-only rewards. Our key insight is to model agentic RAG reasoning as a rollout tree, where each reasoning step naturally maps to a node. This tree structure allows step utility to be estimated via Monte Carlo estimation over its descendant outcomes, yielding fine-grained process advantages without requiring intermediate labels. To make this paradigm practical, we introduce an efficient online tree construction strategy that preserves exploration diversity under a constrained computational budget. With a rollout cost comparable to strong baselines like Search-R1, experiments on seven multi-hop and general QA benchmarks across multiple model scales show that TreePS-RAG consistently outperforms both outcome-supervised and leading process-supervised RL methods.
%U https://aclanthology.org/2026.findings-emnlp.1007/
%P 21297-21316
Markdown (Informal)
[TreePS-RAG: Tree-based Process Supervision for Reinforcement Learning in Agentic RAG](https://aclanthology.org/2026.findings-emnlp.1007/) (Zhang et al., Findings 2026)
ACL
- Tianhua Zhang, Kun Li, Junan Li, Yunxiang Li, Hongyin Luo, Xixin Wu, James R. Glass, and Helen M. Meng. 2026. TreePS-RAG: Tree-based Process Supervision for Reinforcement Learning in Agentic RAG. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 21297–21316, Budapest, Hungary. Association for Computational Linguistics.