Absolute Zero: Reinforced Self-play Reasoning with Zero Data — interactive visual explainer | Rudrite Research

Researchers Zhao et al. published a paper on arXiv 2025 introducing Absolute Zero, a method where a model proposes its own tasks and a code executor grades them, enabling reasoning reinforcement learning with zero human data. An interactive visual explainer of the paper is now available online.

Absolute Zero: Reinforced Self-play Reasoning with Zero Data A model proposes its own tasks and a code executor grades them — reasoning RL with no human data. Zhao et al. · arXiv 2025 · Reasoning & RL. Read the paper ↗ https://arxiv.org/abs/2505.03335 A free, interactive, animated visual explainer of Absolute Zero: Reinforced Self-play Reasoning with Zero Data — every exhibit computed from the real formulas, with verbatim quotes from the source. Questions - What is Absolute Zero: Reinforced Self-play Reasoning with Zero Data? - A model proposes its own tasks and a code executor grades them — reasoning RL with no human data. - Who published Absolute Zero: Reinforced Self-play Reasoning with Zero Data, and where? - Zhao et al. — arXiv 2025 arXiv:2505.03335 . - Where can I find a visual explainer of Absolute Zero: Reinforced Self-play Reasoning with Zero Data? - 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