@inproceedings{guo-etal-2026-turning,
title = "Turning Failures into Value: Negative Experience Replay for {RLVR} via Confidence Gating and Boundary Failure Sampling",
author = "Guo, Jialiang and
Xiong, Fucheng and
He, Xu and
Zhao, Haodong and
li, Xingyang and
Zeng, Ke and
Cai, Xunliang",
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.1682/",
pages = "36316--36334",
ISBN = "979-8-89176-390-6",
abstract = "Reinforcement Learning with Verifiable Rewards (RLVR) has become the standard paradigm for enhancing reasoning capabilities in Large Language Models, yet on-policy algorithms like GRPO suffer from sample inefficiency. Current experience replay methods for RLVR typically replay correct trajectories to consolidate learned reasoning patterns and accelerate convergence, but overlook the vast failure space. This work investigates how to effectively replay failure trajectories. We find that the high heterogeneity of failures renders random replay ineffective, and that high-value negatives should be both gradient-efficient and structurally proximal to correct solutions. To this end, we propose NexGRPO, which employs mid-confidence gating to filter invalid noise and saturated errors, and utilizes boundary failure sampling to retrieve boundary errors semantically similar to correct solutions for targeted refinement. Extensive experiments on mathematical and general reasoning benchmarks demonstrate that NexGRPO outperforms strong baaselines and achieves improved out-of-distribution generalization."
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%0 Conference Proceedings
%T Turning Failures into Value: Negative Experience Replay for RLVR via Confidence Gating and Boundary Failure Sampling
%A Guo, Jialiang
%A Xiong, Fucheng
%A He, Xu
%A Zhao, Haodong
%A li, Xingyang
%A Zeng, Ke
%A Cai, Xunliang
%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 guo-etal-2026-turning
%X Reinforcement Learning with Verifiable Rewards (RLVR) has become the standard paradigm for enhancing reasoning capabilities in Large Language Models, yet on-policy algorithms like GRPO suffer from sample inefficiency. Current experience replay methods for RLVR typically replay correct trajectories to consolidate learned reasoning patterns and accelerate convergence, but overlook the vast failure space. This work investigates how to effectively replay failure trajectories. We find that the high heterogeneity of failures renders random replay ineffective, and that high-value negatives should be both gradient-efficient and structurally proximal to correct solutions. To this end, we propose NexGRPO, which employs mid-confidence gating to filter invalid noise and saturated errors, and utilizes boundary failure sampling to retrieve boundary errors semantically similar to correct solutions for targeted refinement. Extensive experiments on mathematical and general reasoning benchmarks demonstrate that NexGRPO outperforms strong baaselines and achieves improved out-of-distribution generalization.
%U https://aclanthology.org/2026.acl-long.1682/
%P 36316-36334
Markdown (Informal)
[Turning Failures into Value: Negative Experience Replay for RLVR via Confidence Gating and Boundary Failure Sampling](https://aclanthology.org/2026.acl-long.1682/) (Guo et al., ACL 2026)
ACL
- Jialiang Guo, Fucheng Xiong, Xu He, Haodong Zhao, Xingyang li, Ke Zeng, and Xunliang Cai. 2026. Turning Failures into Value: Negative Experience Replay for RLVR via Confidence Gating and Boundary Failure Sampling. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 36316–36334, San Diego, California, United States. Association for Computational Linguistics.