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Rubric-based reinforcement learning (RL) evaluates responses against explicit criteria to construct rewards, offering a viable way to train large language models on open-ended tasks. However, standard methods compress criterion-level feedback into scalar rewards and assign uniform token advantages, leaving structured r...
DE-Venus is presented, a unified framework for data-efficient RLVR that treats supervision as evolving state across data preparation and policy optimization and reduces annotation and training costs without sacrificing scalable RL execution.
FCCA, which estimates the signed input--error cross-covariance, whitens it with diagonal Fisher moments, truncates it in the resulting local metric, maps the selected directions back, and applies thin QR to obtain stable core coordinates, shows that a carefully selected fixed span can recover most of the benefit of mov...
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