Expanding RL with Verifiable Rewards Across Diverse Domains
Authors: name: Yi Su, name: Dian Yu, name: Linfeng Song, name: Juntao Li, name: Haitao Mi, name: Zhaopeng Tu, name: Min Zhang, name: Dong Yu

Reinforcement learning (RL) with verifiable rewards (RLVR) has shown
promising results in mathematical reasoning and coding tasks where
well-structured reference answers are available. However, its applicability to
broader domains remains underexplored. In this work, we study the extension of
RLVR to more diverse domains such as medicine, chemistry, psychology, and
economics. We observe high agreement in binary judgments across different large
language models (LLMs) when objective reference answers exist, which challenges
the necessity of large-scale annotation for training domain-specific reward
models. To address the limitations of binary rewards when handling unstructured
reference answers, we further incorporate model-based soft scoring into RLVR to
improve its flexibility. Our experiments show that a distilled generative
reward model can serve as an effective cross-domain verifier, providing
reliable reward signals for RL without requiring domain-specific annotations.
By fine-tuning a base 7B model using various RL algorithms against our reward
model, we obtain policies that outperform state-of-the-art open-source aligned
LLMs such as Qwen2.5-72B-Instruct and DeepSeek-R1-Distill-Qwen-32B by a large
margin, across domains in free-form answer settings. This also strengthens
RLVR's robustness and scalability, highlighting its potential for real-world
applications with noisy or weak labels.