{"slug": "absolute-zero-reinforced-self-play-reasoning-with-zero-data-interactive-visual", "title": "Absolute Zero: Reinforced Self-play Reasoning with Zero Data — interactive visual explainer | Rudrite Research", "summary": "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.", "body_md": "# Absolute Zero: Reinforced Self-play Reasoning with Zero Data\n\nA model proposes its own tasks and a code executor grades them — reasoning RL with no human data.\n\nZhao et al. · arXiv 2025 · Reasoning & RL. [Read the paper ↗](https://arxiv.org/abs/2505.03335)\n\nA 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.\n\n## Questions\n\n- What is Absolute Zero: Reinforced Self-play Reasoning with Zero Data?\n- A model proposes its own tasks and a code executor grades them — reasoning RL with no human data.\n- Who published Absolute Zero: Reinforced Self-play Reasoning with Zero Data, and where?\n- Zhao et al. — arXiv 2025 (arXiv:2505.03335).\n- Where can I find a visual explainer of Absolute Zero: Reinforced Self-play Reasoning with Zero Data?\n- Right here — a free, interactive, animated walkthrough of the whole paper, with exhibits computed from the real formulas and verbatim quotes from the source.\n\n## Related explainers\n\n[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)", "url": "https://wpnews.pro/news/absolute-zero-reinforced-self-play-reasoning-with-zero-data-interactive-visual", "canonical_source": "https://research.rudrite.com/absolute-zero", "published_at": "2026-06-15 00:00:00+00:00", "updated_at": "2026-06-15 14:16:49.690158+00:00", "lang": "en", "topics": ["large-language-models", "ai-research", "machine-learning"], "entities": ["Zhao et al.", "arXiv", "Absolute Zero"], "alternates": {"html": "https://wpnews.pro/news/absolute-zero-reinforced-self-play-reasoning-with-zero-data-interactive-visual", "markdown": "https://wpnews.pro/news/absolute-zero-reinforced-self-play-reasoning-with-zero-data-interactive-visual.md", "text": "https://wpnews.pro/news/absolute-zero-reinforced-self-play-reasoning-with-zero-data-interactive-visual.txt", "jsonld": "https://wpnews.pro/news/absolute-zero-reinforced-self-play-reasoning-with-zero-data-interactive-visual.jsonld"}}