@inproceedings{zhao-etal-2026-consolidation,
title = "Consolidation or Adaptation? {PRISM}: Disentangling {SFT} and {RL} Data via Gradient Concentration",
author = "Zhao, Yang and
Ouyang, Yangou and
Ding, Xiao and
Wang, Hepeng and
Cai, Bibo and
Xiong, Kai and
Gao, Jinglong and
Sun, Zhouhao and
Du, Li and
Qin, Bing and
Liu, Ting",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1097/",
pages = "23929--23941",
ISBN = "979-8-89176-390-6",
abstract = "While Hybrid Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) has become the standard paradigm for training LLM agents, effective mechanisms for data allocation between these stages remain largely underexplored. Current data arbitration strategies often rely on surface-level heuristics that fail to diagnose intrinsic learning needs. Since SFT targets pattern consolidation through imitation while RL drives structural adaptation via exploration, misaligning data with these functional roles causes severe optimization interference. We propose PRISM, a dynamics-aware framework grounded in Schema Theory that arbitrates data based on its degree of cognitive conflict with the model{'}s existing knowledge. By analyzing the spatial geometric structure of gradients, PRISM identifies data triggering high spatial concentration as high-conflict signals that require RL for structural restructuring. In contrast, data yielding diffuse updates is routed to SFT for efficient consolidation. Extensive experiments on WebShop and ALFWorld demonstrate that PRISM achieves a Pareto improvement, outperforming state-of-the-art hybrid methods while reducing computational costs by up to 3.22 $\times$. Our findings suggest that disentangling data based on internal optimization regimes is crucial for scalable and robust agent alignment."
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<abstract>While Hybrid Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) has become the standard paradigm for training LLM agents, effective mechanisms for data allocation between these stages remain largely underexplored. Current data arbitration strategies often rely on surface-level heuristics that fail to diagnose intrinsic learning needs. Since SFT targets pattern consolidation through imitation while RL drives structural adaptation via exploration, misaligning data with these functional roles causes severe optimization interference. We propose PRISM, a dynamics-aware framework grounded in Schema Theory that arbitrates data based on its degree of cognitive conflict with the model’s existing knowledge. By analyzing the spatial geometric structure of gradients, PRISM identifies data triggering high spatial concentration as high-conflict signals that require RL for structural restructuring. In contrast, data yielding diffuse updates is routed to SFT for efficient consolidation. Extensive experiments on WebShop and ALFWorld demonstrate that PRISM achieves a Pareto improvement, outperforming state-of-the-art hybrid methods while reducing computational costs by up to 3.22 \times. Our findings suggest that disentangling data based on internal optimization regimes is crucial for scalable and robust agent alignment.</abstract>
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%0 Conference Proceedings
%T Consolidation or Adaptation? PRISM: Disentangling SFT and RL Data via Gradient Concentration
%A Zhao, Yang
%A Ouyang, Yangou
%A Ding, Xiao
%A Wang, Hepeng
%A Cai, Bibo
%A Xiong, Kai
%A Gao, Jinglong
%A Sun, Zhouhao
%A Du, Li
%A Qin, Bing
%A Liu, Ting
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F zhao-etal-2026-consolidation
%X While Hybrid Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) has become the standard paradigm for training LLM agents, effective mechanisms for data allocation between these stages remain largely underexplored. Current data arbitration strategies often rely on surface-level heuristics that fail to diagnose intrinsic learning needs. Since SFT targets pattern consolidation through imitation while RL drives structural adaptation via exploration, misaligning data with these functional roles causes severe optimization interference. We propose PRISM, a dynamics-aware framework grounded in Schema Theory that arbitrates data based on its degree of cognitive conflict with the model’s existing knowledge. By analyzing the spatial geometric structure of gradients, PRISM identifies data triggering high spatial concentration as high-conflict signals that require RL for structural restructuring. In contrast, data yielding diffuse updates is routed to SFT for efficient consolidation. Extensive experiments on WebShop and ALFWorld demonstrate that PRISM achieves a Pareto improvement, outperforming state-of-the-art hybrid methods while reducing computational costs by up to 3.22 \times. Our findings suggest that disentangling data based on internal optimization regimes is crucial for scalable and robust agent alignment.
%U https://aclanthology.org/2026.acl-long.1097/
%P 23929-23941
Markdown (Informal)
[Consolidation or Adaptation? PRISM: Disentangling SFT and RL Data via Gradient Concentration](https://aclanthology.org/2026.acl-long.1097/) (Zhao et al., ACL 2026)
ACL
- Yang Zhao, Yangou Ouyang, Xiao Ding, Hepeng Wang, Bibo Cai, Kai Xiong, Jinglong Gao, Zhouhao Sun, Li Du, Bing Qin, and Ting Liu. 2026. Consolidation or Adaptation? PRISM: Disentangling SFT and RL Data via Gradient Concentration. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 23929–23941, San Diego, California, United States. Association for Computational Linguistics.