AIBullisharXiv – CS AI · Apr 146/10
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Low-rank Optimization Trajectories Modeling for LLM RLVR Acceleration
Researchers propose NExt, a nonlinear extrapolation framework that accelerates reinforcement learning with verifiable rewards (RLVR) for large language models by modeling low-rank parameter trajectories. The method reduces computational overhead by approximately 37.5% while remaining compatible with various RLVR algorithms, addressing a key bottleneck in scaling LLM training.