---
title: "Rubric Curriculum RL: Exploiting the Generation-Verification Gap in Non-Verifiable Domains"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.06244v1"
permalink: "https://feed.astralor.com/items/itm_a563dae8dc74ee2183f9394c"
published: "2026-11-04T09:51:06.000Z"
score: 58
channel: "ai"
tags: ["Rubric Curriculum","Exploiting","Generation-Verification Gap","Non-Verifiable Domains Reinforcement","RLVR","Extending","Reinforcement"]
---

# Rubric Curriculum RL: Exploiting the Generation-Verification Gap in Non-Verifiable Domains

## 速览

Reinforcement learning with verifiable rewards (RLVR) has led to significant gains in math and code generation. Extending these gains to open-ended domains remains challenging: ground-truth verification is unavailable, …

## 关键点

- Reinforcement learning with verifiable rewards (RLVR) has led to significant gains in math and code generation.
- Extending these gains to open-ended domains remains challenging: ground-truth verification is unavailable, human annotation is expensive, and learned reward models are prone to reward hacking.
- The generation-verification gap, where judging good outputs is easier than producing them, offers a potential path to improvement.

## 全文

Reinforcement learning with verifiable rewards (RLVR) has led to significant gains in math and code generation. Extending these gains to open-ended domains remains challenging: ground-truth verification is unavailable, human annotation is expensive, and learned reward models are prone to reward hacking. The generation-verification gap, where judging good outputs is easier than producing them, offers a potential path to improvement. However, rubric-based judges are often imprecise and stationary, so policies can overfit or reward hack them. We introduce Rubric Curriculum RL (RcRL), which exploits this gap without new data, human annotations, or stronger teacher models. We combine pairwise preferences over rollouts with a curriculum over rubric criteria, yielding a more discriminative signal than absolute scoring and a non-stationary objective. Whereas baselines plateau within a few hundred steps, RcRL preserves output entropy and keeps improving past 1000 steps. On creative writing, RcRL achieves a 70.5% human win rate against the base model, with gains across benchmarks and judges. On HealthBench, RcRL improves over the base model by 14.6% (full set) and 25.4% (consensus subset), outperforming all training baselines, including instance-specific rubrics.

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