arXiv:2606.27369v1 Announce Type: new 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 \textbf{R}anking-\textbf{i}nduced \textbf{VER}ifiable 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 rewards, we identify two key challenges: \emph
The continuous drive for more autonomous and capable AI systems motivates research into advanced training paradigms like RiVER to overcome previous limitations of RL with ground-truth dependencies.
This research addresses a fundamental limitation in reinforcement learning for LLMs, enabling their application to a broader range of complex, real-world problems where explicit ground-truth solutions are unavailable.
LLMs can now be trained and optimized for tasks requiring sophisticated reasoning and problem-solving without needing human-labeled, correct answers, expanding their utility and autonomy.
- · AI developers
- · LLM-powered applications
- · Automation companies
- · Tasks reliant on human-labeled correct answers
- · Traditional RL methods with ground-truth dependencies
LLMs become more capable in solving ill-defined or open-ended problems, reducing reliance on human supervision for complex tasks.
The ability to train without ground-truth solutions accelerates the development of more autonomous and intelligent AI agents.
This could lead to a proliferation of AI agents capable of performing a wider array of white-collar and specialized workflows, impacting various sectors.
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Read at arXiv cs.LG