Summary
This forum post introduces RiVER (Ranking-induced VERifiable framework), a research paper (arXiv 2606.27369) by Yingyu Lin, Qiyue Gao, and Nikki Lijing Kuang. Standard reinforcement learning with verifiable rewards (RLVR) for training large language models depends on ground-truth answers to assign rewards, restricting its use to tasks where reference solutions are available. RiVER extends RLVR to score-based optimization tasks that lack ground-truth solutions by using deterministic execution feedback as continuous-valued supervision, combined with group-relative RL. The framework allows LLMs to improve on tasks where no known correct answer exists, broadening the applicability of verifiable-reward reinforcement learning beyond math and other verifiable domains.
Paper Overview
Research field: Machine Learning
Authors: Yingyu Lin, Qiyue Gao, Nikki Lijing Kuang
Published: 2026-06-27
arXiv:
2606.27369Summary
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. When applying group-relative RL to such continuous feedback signals, RiVER enables effective training without reference answers, extending RLVR's reach to broader problem domains.
Key Points
- RLVR normally requires ground-truth answers, restricting which tasks it can be applied to.
- RiVER replaces ground-truth supervision with deterministic execution feedback as a continuous-valued reward signal.
- The method targets score-based optimization tasks rather than binary correctness checks.
- Group-relative RL is applied to the continuous feedback for training LLMs.
- RiVER broadens the applicability of RLVR to tasks where no known correct solution exists.
Links
- Paper: https://arxiv.org/abs/2606.27369
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*Automatically collected on 2026-06-27*
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