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📄 ResearchMay 12, 2026

Trust the Batch, On- or Off-Policy: Adaptive Policy Optimization for RL Post-Training

Reinforcement learning is structurally harder than supervised learning because the policy changes the data distribution it learns from. The resulting fragility is especially visible in large-model training, where the training and rollout systems differ in numerical precision, sampling, and other imp...

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Source

http://arxiv.org/abs/2605.12380v1