CATPO introduces an informativeness score F(T) and critique-guided healing for failed trees to improve efficiency and performance in tree-based RLVR, reaching 37.5% macro accuracy on math benchmarks.
Rethinking the sampling criteria in reinforcement learning for LLM reasoning: A competence-difficulty alignment perspective.arXiv preprint arXiv:2505.17652,
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CATPO: Critique-Augmented Tree Policy Optimization
CATPO introduces an informativeness score F(T) and critique-guided healing for failed trees to improve efficiency and performance in tree-based RLVR, reaching 37.5% macro accuracy on math benchmarks.