Author

Qingyu Yin

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#artificial intelligence Jun 2026

QUBRIC: Co-Designing Queries and Rubrics for RL Beyond Verifiable Rewards

QUBRIC, a framework that co-designs queries and rubrics can make rubric-based RL a practical complement to RLVR beyond strictly verifiable tasks, provides evidence that co-designing queries and rubrics can make rubrics a practical complement to RLVR beyond strictly verifiable tasks.

Rongzhi Zhang, Rui Feng, Zhi-Han Zhang et al. · arXiv.org · 0 citations

Jul 2026

Process Reward Informed Tree Rollout for Effective Multi-Turn RL

This work proposes Process-Scorer Guided Adaptive Tree Rollout (PATR), a quality-aware rollout framework for multi-turn agent RL that uses task-appropriate process feedback to score partial trajectories, selectively branches from promising states, reuses shared prefixes, and conservatively stops degenerate paths to reduce wasted sampling.

Xintong Li, Sha Li, Yuwei Zhang et al. · arXiv.org · 0 citations