RiVER: Reinforcement Learning Without Ground-Truth Solutions Can Improve LLMs

Forum topic · 小凯 · 2026-06-27

Summary

A forum post on zhichai.net introduces the arXiv paper 'Reinforcement Learning without Ground-Truth Solutions can Improve LLMs' (arXiv:2606.27369) by Yingyu Lin, Qiyue Gao, and Nikki Lijing Kuang. The paper addresses a core limitation of reinforcement learning with verifiable rewards (RLVR): its dependence on ground-truth answers for reward assignment, which restricts applicability to tasks where correct solutions are unknown. The authors propose RiVER (Ranking-induced VERifiable framework), which trains LLMs on score-based optimization tasks without ground-truth solutions, using deterministic execution feedback as continuous-valued supervision. The paper further discusses how group-relative RL can be applied to such continuous rewards. This post includes the paper overview, author list, publication date, arXiv link, and both Chinese and original English abstracts, automatically collected on 2026-06-27.

Paper Overview

Field: Machine Learning Authors: Yingyu Lin, Qiyue Gao, Nikki Lijing Kuang Published: 2026-06-27 arXiv: 2606.27369

Chinese Abstract (translated)

Reinforcement learning with verifiable rewards (RLVR) for training LLMs typically relies on ground-truth answers to assign rewards, limiting its applicability to tasks where the ground-truth solution is unknown. The authors introduce a Ranking-induced VERifiable framework (RiVER) that trains LLMs on score-based optimization tasks without ground-truth solutions, using deterministic execution feedback as continuous-valued supervision.

Original Abstract

Reinforcement learning with verifiable rewards (RLVR) for training LLMs typically rely on ground-truth answers to assign rewards, limiting their applicability to tasks where the ground-truth solution is unknown. We introduce a Ranking-induced VERifiable framework (RiVER) that trains LLMs on score-based optimization tasks without ground-truth solutions, using deterministic execution feedback as continuous-valued supervision. When applying group-relative RL to such conti...

*(The abstract is truncated in the source post; see the arXiv link above for the full version.)*

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*Automatically collected on 2026-06-27.*