@inproceedings{li-etal-2026-wist,
title = "{WIST}: Web-Grounded Iterative Self-Play Tree for Domain-Targeted Reasoning Improvement",
author = "Li, Fangyuan and
Li, Pengfei and
Wang, Shijie and
Gao, Junqi and
Liu, Jianxing and
Qi, Biqing and
Li, Yuqiang",
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.1456/",
pages = "31563--31585",
ISBN = "979-8-89176-390-6",
abstract = "Recent progress in reinforcement learning with verifiable rewards (RLVR) offers a practical path to self-improving language models, but existing methods face a key trade-off: endogenous self-play can drift over iterations, while corpus-grounded approaches rely on curated data environments. We present WIST, a Web-grounded Iterative Self-play Tree framework for domain-targeted reasoning improvement that learns directly from the open-web without requiring any pre-arranged domain corpus. WIST incrementally expands a domain tree to structure exploration and retrieves and cleans path-consistent web evidence to construct a controllable training environment. It then performs Challenger-Solver self-play with verifiable rewards, and feeds learnability signals back to update node posteriors and guide subsequent exploration through an adaptive curriculum. Across four backbones, WIST consistently improves over the base models and typically outperforms both purely endogenous self-evolution and corpus-grounded self-play baselines, with the Overall gains reaching +9.8 (Qwen3-4B-Base) and +9.7 (OctoThinker-8B-Hybrid-Base). WIST is also domain-steerable: improving Qwen3-8B-Base by +14.79 in medicine and Qwen3-4B-Base by +5.28 on PhyBench. Ablations further confirm the importance of WIST{'}s key components for stable open-web learning. Our Code is available at https://github.com/lfy-123/WIST."
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<abstract>Recent progress in reinforcement learning with verifiable rewards (RLVR) offers a practical path to self-improving language models, but existing methods face a key trade-off: endogenous self-play can drift over iterations, while corpus-grounded approaches rely on curated data environments. We present WIST, a Web-grounded Iterative Self-play Tree framework for domain-targeted reasoning improvement that learns directly from the open-web without requiring any pre-arranged domain corpus. WIST incrementally expands a domain tree to structure exploration and retrieves and cleans path-consistent web evidence to construct a controllable training environment. It then performs Challenger-Solver self-play with verifiable rewards, and feeds learnability signals back to update node posteriors and guide subsequent exploration through an adaptive curriculum. Across four backbones, WIST consistently improves over the base models and typically outperforms both purely endogenous self-evolution and corpus-grounded self-play baselines, with the Overall gains reaching +9.8 (Qwen3-4B-Base) and +9.7 (OctoThinker-8B-Hybrid-Base). WIST is also domain-steerable: improving Qwen3-8B-Base by +14.79 in medicine and Qwen3-4B-Base by +5.28 on PhyBench. Ablations further confirm the importance of WIST’s key components for stable open-web learning. Our Code is available at https://github.com/lfy-123/WIST.</abstract>
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%0 Conference Proceedings
%T WIST: Web-Grounded Iterative Self-Play Tree for Domain-Targeted Reasoning Improvement
%A Li, Fangyuan
%A Li, Pengfei
%A Wang, Shijie
%A Gao, Junqi
%A Liu, Jianxing
%A Qi, Biqing
%A Li, Yuqiang
%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 li-etal-2026-wist
%X Recent progress in reinforcement learning with verifiable rewards (RLVR) offers a practical path to self-improving language models, but existing methods face a key trade-off: endogenous self-play can drift over iterations, while corpus-grounded approaches rely on curated data environments. We present WIST, a Web-grounded Iterative Self-play Tree framework for domain-targeted reasoning improvement that learns directly from the open-web without requiring any pre-arranged domain corpus. WIST incrementally expands a domain tree to structure exploration and retrieves and cleans path-consistent web evidence to construct a controllable training environment. It then performs Challenger-Solver self-play with verifiable rewards, and feeds learnability signals back to update node posteriors and guide subsequent exploration through an adaptive curriculum. Across four backbones, WIST consistently improves over the base models and typically outperforms both purely endogenous self-evolution and corpus-grounded self-play baselines, with the Overall gains reaching +9.8 (Qwen3-4B-Base) and +9.7 (OctoThinker-8B-Hybrid-Base). WIST is also domain-steerable: improving Qwen3-8B-Base by +14.79 in medicine and Qwen3-4B-Base by +5.28 on PhyBench. Ablations further confirm the importance of WIST’s key components for stable open-web learning. Our Code is available at https://github.com/lfy-123/WIST.
%U https://aclanthology.org/2026.acl-long.1456/
%P 31563-31585
Markdown (Informal)
[WIST: Web-Grounded Iterative Self-Play Tree for Domain-Targeted Reasoning Improvement](https://aclanthology.org/2026.acl-long.1456/) (Li et al., ACL 2026)
ACL
- Fangyuan Li, Pengfei Li, Shijie Wang, Junqi Gao, Jianxing Liu, Biqing Qi, and Yuqiang Li. 2026. WIST: Web-Grounded Iterative Self-Play Tree for Domain-Targeted Reasoning Improvement. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 31563–31585, San Diego, California, United States. Association for Computational Linguistics.