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Machine Learning

Reinforcement Learning without Ground-Truth Solutions can Improve LLMs

Yingyu Lin, Qiyue Gao, Nikki Lijing Kuang, Xunpeng Huang, Kun Zhou, Tongtong Liang, Zhewei Yao, Yi-An Ma, Yuxiong He

Featured June 29, 2026

Original Paper

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Simply

RiVER teaches AI to solve complex puzzles by comparing different attempts against each other instead of needing a perfect answer key, focusing on the best solutions to improve overall reasoning.

In depth
The paper introduces RiVER, a framework that enables LLMs to learn from executable optimization tasks without requiring ground-truth solutions. By employing instance-wise ranking and winner-heavy reward shaping, the method mitigates scale and frequency dominance, allowing the model to derive stable, fine-grained learning signals from relative solution quality.

Key Takeaways

  • 1
    RiVER enables effective training on score-based optimization tasks where ground-truth solutions are unknown.
  • 2
    The framework uses instance-wise ranking to eliminate arbitrary score-scale effects across different test instances.
  • 3
    Winner-heavy reward shaping focuses policy updates on the strongest discovered solvers, preventing suboptimal heuristics from dominating the gradient.

Conceptual Flow

HIGH LEVEL
1
Methodology

The system compares multiple AI-generated solutions on the same task to see which one performs best.

Multiple AI Attempts
Rank and Shape Rewards
Improved AI Policy
2
Results

The AI learns to write better code and solve harder problems by practicing on optimization tasks.

Basic Reasoning

Learn from Optimization

Advanced Coding Skills