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.

ProRL: Prolonged RL Expands Reasoning Boundaries 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 ↗ https://arxiv.org/abs/2505.24864 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. Related explainers DeepSeek-R1 /deepseek-r1 Chain-of-Thought Prompting Elicits Reasoning in Large Language Models /chain-of-thought Training language models to follow instructions with human feedback /instructgpt Direct Preference Optimization: Your Language Model is Secretly a Reward Model /dpo DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models /deepseekmath Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters /test-time-compute Constitutional AI: Harmlessness from AI Feedback /constitutional-ai DAPO: An Open-Source LLM Reinforcement Learning System at Scale /dapo