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Rubric Curriculum RL Improves RLVR in Non-Verifiable Domains

The paper introduces Rubric Curriculum RL (RcRL), a method that exploits the generation-verification gap in domains without ground-truth rewards by combining pairwise preferences over rollouts with a curriculum over rubric criteria. This creates a more discriminative and non-stationary training signal, avoiding the plateau seen in baselines. RcRL achieves a 70.5% human win rate on creative writing and improves HealthBench performance by 14.6% (full set) and 25.4% (consensus subset), outperforming all training baselines.

Why it matters: This presents a concrete, novel training method with measurable gains in open-ended domains, addressing a key limitation of RLVR.

arxiv13h agoScore 82

Reinforcement LearningReward ModelingLLM Alignment

Research

Feeling of Knowing in Large Language Models

This paper introduces a functional 'feeling of knowing' (FoK) mechanism for LLMs to align answerability judgments with post-training knowledge updates. It proposes two trainable channels—knowledge and FoK—trained via three stages: on-policy supervised fine-tuning, knowledge-delta FoK training, and FoK-guided policy optimization. Experiments on factual QA and sequential updates show improved judgment quality without sacrificing capability on MMLU and ARC-Easy.

arxiv1d agoScore 78

LLM alignmentknowledge judgmentmodel routing