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Lin Wang, Yang Zhang, Jingfan Chen, Xiaoyan Zhao, Fengbin Zhu, Qing Li, Tat-Seng Chua · 2026 · arXiv 2602.04278

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cs.LG 1

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2026 1

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Adaptive Loss Balancing for Noise-Robust GRPO in Generative Recommendation

cs.LG · 2026-06-07 · unverdicted · novelty 5.0

AdaGRPO gates GRPO reinforcement learning with supervised NLL using per-sample binary clips based on policy difficulty and reward discriminability, raising HR@10 from 11.01% to 12.18% while keeping hallucination below 0.22% on large-scale e-commerce data and showing A/B gains.

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  • Adaptive Loss Balancing for Noise-Robust GRPO in Generative Recommendation cs.LG · 2026-06-07 · unverdicted · none · ref 22

    AdaGRPO gates GRPO reinforcement learning with supervised NLL using per-sample binary clips based on policy difficulty and reward discriminability, raising HR@10 from 11.01% to 12.18% while keeping hallucination below 0.22% on large-scale e-commerce data and showing A/B gains.