arXiv AI
CARE: Contrastive Anchor-based Rubric Evolution for Large Language Model Post-Training
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arXiv AI
Aug 13
Rubric Dropout: A Simple Way to Mitigate Reward Hacking in Rubric-as-Reward RL
arXiv:2608. 11669v1 Announce Type: cross Abstract: Reinforcement learning against rubrics, lists of criteria graded by an LLM judge, has become a standard way to post-train language models on tasks with no deterministic answer.
arXiv AI
Aug 17
Inducing Reward-Free Judging Rubrics that Reduce Over-Crediting in Agent Evaluation
arXiv:2608. 13564v1 Announce Type: new Abstract: Evaluating language-model agents at scale increasingly relies on a second language model as an automatic judge, because the gold signal, an executable environment reward, is expensive, slow, or unavailable at deployment time.
arXiv AI
Jul 21
Enhancing Rubric-based RL via Self-Distillation
arXiv:2607. 18082v1 Announce Type: cross Abstract: Rubric-based RL has recently shown promise in improving LLMs on open-ended tasks.
arXiv Machine Learning
Aug 4
CriPO: Enhancing Rubric-based RL via Self-Distillation
arXiv:2607. 18082v3 Announce Type: replace Abstract: Rubric-based RL has recently shown promise in improving LLMs on open-ended tasks.
arXiv Machine Learning
Oct 9
Evaluating Rubric Generation with Interventional Transfer
The paper introduces Interventional Transfer (IT), a method for evaluating rubric generation by assessing how two rubrics respond to perturbations that cause a pass/fail shift. IT reveals asymmetries in rubrics generated by different LLMs when applied to HealthBench, showing that improvements on one rubric do not reliably transfer to the other. The study contrasts IT with existing evaluation approaches, highlighting its ability to uncover these asymmetries.
arXiv Computation and Language
Sep 30
AdaTutoRank: Learning to Rerank Document Sets via Adaptive Tutoring Optimization for RAG and Deep Research
AdaTutoRank introduces a setwise document reranker that uses Adaptive Tutoring Optimization (ATO) to provide graded supervision across nine rubric dimensions. By generating hint‑based silver labels, reinforcement rewards, and distillation cues tailored to each rollout’s quality, the method improves credit assignment for individual documents within a set. Experiments on ten benchmarks covering Retrieval‑Augmented Generation (RAG), deep research, and setwise evaluation show that AdaTutoRank achieves superior overall performance while reducing the number of retrieval calls.