@inproceedings{yang-etal-2026-harmonizing,
title = "Harmonizing Dense and Sparse Signals in Multi-turn {RL}: Dual-Horizon Credit Assignment for Industrial Sales Agents",
author = "Yang, Haojin and
Jian, Ai and
Wang, Yiwei and
Huang, Xinyue and
Zhang, Weipeng and
Zeng, Ke and
Cai, Xunliang and
Ruan, Jingqing",
editor = "Li, Yunyao and
Rehm, Georg and
Tu, Mei",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-industry.74/",
pages = "1067--1076",
ISBN = "979-8-89176-394-4",
abstract = "Optimizing large language models for industrial sales requires balancing long-term commercial objectives (e.g., conversion rate) with immediate linguistic constraints such as fluency and compliance. Conventional reinforcement learning often merges these heterogeneous goals into a single reward, causing high-magnitude session-level rewards to overwhelm subtler turn-level signals, which leads to unstable training or reward hacking.To address this issue, we propose **Dual-Horizon Credit Assignment (DuCA)**, a framework that disentangles optimization across time scales. Its core, **Horizon-Independent Advantage Normalization (HIAN)**, separately normalizes advantages from turn-level and session-level rewards before fusion, ensuring balanced gradient contributions from both immediate and long-term objectives to the policy update.Extensive experiments with a high-fidelity user simulator show DuCA outperforms the state-of-the-art GRPO baseline, achieving a 6.82{\%} relative improvement in conversion rate, reducing inter-sentence repetition by 82.28{\%}, and lowering identity detection rate by 27.35{\%}, indicating a substantial improvement for an industrial sales scenario that effectively balances the dual demands of strategic performance and naturalistic language generation."
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<abstract>Optimizing large language models for industrial sales requires balancing long-term commercial objectives (e.g., conversion rate) with immediate linguistic constraints such as fluency and compliance. Conventional reinforcement learning often merges these heterogeneous goals into a single reward, causing high-magnitude session-level rewards to overwhelm subtler turn-level signals, which leads to unstable training or reward hacking.To address this issue, we propose **Dual-Horizon Credit Assignment (DuCA)**, a framework that disentangles optimization across time scales. Its core, **Horizon-Independent Advantage Normalization (HIAN)**, separately normalizes advantages from turn-level and session-level rewards before fusion, ensuring balanced gradient contributions from both immediate and long-term objectives to the policy update.Extensive experiments with a high-fidelity user simulator show DuCA outperforms the state-of-the-art GRPO baseline, achieving a 6.82% relative improvement in conversion rate, reducing inter-sentence repetition by 82.28%, and lowering identity detection rate by 27.35%, indicating a substantial improvement for an industrial sales scenario that effectively balances the dual demands of strategic performance and naturalistic language generation.</abstract>
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%0 Conference Proceedings
%T Harmonizing Dense and Sparse Signals in Multi-turn RL: Dual-Horizon Credit Assignment for Industrial Sales Agents
%A Yang, Haojin
%A Jian, Ai
%A Wang, Yiwei
%A Huang, Xinyue
%A Zhang, Weipeng
%A Zeng, Ke
%A Cai, Xunliang
%A Ruan, Jingqing
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F yang-etal-2026-harmonizing
%X Optimizing large language models for industrial sales requires balancing long-term commercial objectives (e.g., conversion rate) with immediate linguistic constraints such as fluency and compliance. Conventional reinforcement learning often merges these heterogeneous goals into a single reward, causing high-magnitude session-level rewards to overwhelm subtler turn-level signals, which leads to unstable training or reward hacking.To address this issue, we propose **Dual-Horizon Credit Assignment (DuCA)**, a framework that disentangles optimization across time scales. Its core, **Horizon-Independent Advantage Normalization (HIAN)**, separately normalizes advantages from turn-level and session-level rewards before fusion, ensuring balanced gradient contributions from both immediate and long-term objectives to the policy update.Extensive experiments with a high-fidelity user simulator show DuCA outperforms the state-of-the-art GRPO baseline, achieving a 6.82% relative improvement in conversion rate, reducing inter-sentence repetition by 82.28%, and lowering identity detection rate by 27.35%, indicating a substantial improvement for an industrial sales scenario that effectively balances the dual demands of strategic performance and naturalistic language generation.
%U https://aclanthology.org/2026.acl-industry.74/
%P 1067-1076
Markdown (Informal)
[Harmonizing Dense and Sparse Signals in Multi-turn RL: Dual-Horizon Credit Assignment for Industrial Sales Agents](https://aclanthology.org/2026.acl-industry.74/) (Yang et al., ACL 2026)
ACL
- Haojin Yang, Ai Jian, Yiwei Wang, Xinyue Huang, Weipeng Zhang, Ke Zeng, Xunliang Cai, and Jingqing Ruan. 2026. Harmonizing Dense and Sparse Signals in Multi-turn RL: Dual-Horizon Credit Assignment for Industrial Sales Agents. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 1067–1076, San Diego, California, USA. Association for Computational Linguistics.