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cs.CL2026
Regime-Aware Peer Specialization for Robust RAG under Heterogeneous Knowledge Conflicts
Bo Wang, Heyan Huang, Yaolin Li +5
Retrieval-augmented generation (RAG) improves language models by grounding generation in external context. However, it can be fragile when the retrieved context conflicts with the…
cs.LG2026
Spend Your Rollouts Where It Counts: Rollout Allocation for Group-Based RL Post-Training
Woojeong Kim, Ziyi Yang, Jing Nathan Yan +1
Reinforcement learning (RL) is the dominant paradigm for post-training large language models. However, in the online, on-policy setting, rollout generation dominates the computatio…