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ActReview is a rebuttal-guided post-training framework that generates diagnostic claims and revision suggestions for peer reviews by leveraging author responses as supervision, along with a human-curated benchmark for evaluation.
This paper studies the trade-off between grounding and coverage in long-form hallucination reinforcement learning, proposing rubric-based rewards to represent required and optional information for questions. A soft combination of grounding, rubric coverage, and relevance yields the best balance between support and richness.
This paper proposes RLAES, a unified LLM framework that jointly optimizes essay scoring and feedback generation via reinforcement learning with rubric-based rewards, achieving state-of-the-art scoring performance on the ASAP benchmark while maintaining high-quality feedback.
This paper introduces POW3R, a policy-aware rubric reward framework for reinforcement learning with verifiable rewards (RLVR). It shows that static rubric aggregation misallocates learning signal, and POW3R achieves faster convergence and better performance across multiple settings.