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ProRL: Prolonged RL Expands Reasoning Boundaries — interactive visual explainer | Rudrite Research

Researchers Liu et al. published a paper on arXiv 2025 introducing ProRL, a method using prolonged reinforcement learning with KL resets to expand reasoning boundaries in AI models. An interactive visual explainer of the paper is available online.

read1 min publishedJun 13, 2026

Prolonged RL with KL resets expands what a reasoning model can do, not just sharpens it.

Liu et al. · arXiv 2025 · Reasoning & RL. Read the paper ↗ A free, interactive, animated visual explainer of ProRL: Prolonged RL Expands Reasoning Boundaries — every exhibit computed from the real formulas, with verbatim quotes from the source.

Questions #

  • What is ProRL: Prolonged RL Expands Reasoning Boundaries?
  • Prolonged RL with KL resets expands what a reasoning model can do, not just sharpens it.
  • Who published ProRL: Prolonged RL Expands Reasoning Boundaries, and where?
  • Liu et al. — arXiv 2025 (arXiv:2505.24864).
  • Where can I find a visual explainer of ProRL: Prolonged RL Expands Reasoning Boundaries?
  • Right here — a free, interactive, animated walkthrough of the whole paper, with exhibits computed from the real formulas and verbatim quotes from the source.

DeepSeek-R1Chain-of-Thought Prompting Elicits Reasoning in Large Language ModelsTraining language models to follow instructions with human feedbackDirect Preference Optimization: Your Language Model is Secretly a Reward ModelDeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language ModelsScaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model ParametersConstitutional AI: Harmlessness from AI FeedbackDAPO: An Open-Source LLM Reinforcement Learning System at Scale

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