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.
How difficulty-aware staged reinforcement learning enhances llms’ reasoning capabilities: A preliminary experimental study.arXiv preprint arXiv:2504.00829
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Proposes Near-boundary Stochastic Rescue (NSR) as a stochastic modification to clipping in RLVR that recovers near-boundary signals and yields gains over baselines like DAPO and GSPO.
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Adaptive Loss Balancing for Noise-Robust GRPO in Generative Recommendation
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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Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals
Proposes Near-boundary Stochastic Rescue (NSR) as a stochastic modification to clipping in RLVR that recovers near-boundary signals and yields gains over baselines like DAPO and GSPO.