We have 3 of 12 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
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...
Criterion-Distilled Policy Optimization (CriPO), which enhances rubric-based RL via on-policy self-distillation via on-policy self-distillation, to simultaneously address both unexplored and suppressed criteria.
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.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
