{"id":"arxiv:2601.07055","title":"Dr. Zero: Self-Evolving Search Agents without Training Data","date":"2026-01-11","section":"Agents, open-endedness, AGI","quality":0.057854,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3baa3e7fc5768b75e4ef3e64c3cc5297fa42ef19b03bc918ef5352e437e1c168/","source_url":"https://arxiv.org/abs/2601.07055","text_available":true,"authors":["Zhenrui Yue","Kartikeya Upasani","Xianjun Yang","Suyu Ge"],"outgoing":[{"source":"arxiv:2601.07055","target":"arxiv:2402.03300","evidence":"id","id":"arxiv:2402.03300","title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/636fb871904bc06f742cfdea069796f9e9665f95bd58dc63260eaff5154ef985/"},{"source":"arxiv:2601.07055","target":"arxiv:2503.14476","evidence":"id","id":"arxiv:2503.14476","title":"DAPO: An Open-Source LLM Reinforcement Learning System at 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Data","offset":0,"level":1,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3baa3e7fc5768b75e4ef3e64c3cc5297fa42ef19b03bc918ef5352e437e1c168/part-1.html#text"},{"title":"Abstract","offset":63,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3baa3e7fc5768b75e4ef3e64c3cc5297fa42ef19b03bc918ef5352e437e1c168/part-1.html#text"},{"title":"1 Introduction","offset":1820,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3baa3e7fc5768b75e4ef3e64c3cc5297fa42ef19b03bc918ef5352e437e1c168/part-1.html#text"},{"title":"2 Related Work","offset":7499,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3baa3e7fc5768b75e4ef3e64c3cc5297fa42ef19b03bc918ef5352e437e1c168/part-1.html#text"},{"title":"2.1 Reinforcement 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Setup","offset":11206,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3baa3e7fc5768b75e4ef3e64c3cc5297fa42ef19b03bc918ef5352e437e1c168/part-1.html#text"},{"title":"3.2 Proposer Training","offset":13324,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3baa3e7fc5768b75e4ef3e64c3cc5297fa42ef19b03bc918ef5352e437e1c168/part-1.html#text"},{"title":"3.3 Solver Training","offset":18714,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3baa3e7fc5768b75e4ef3e64c3cc5297fa42ef19b03bc918ef5352e437e1c168/part-2.html#text"},{"title":"3.4 The Self-Evolving Dr. Zero","offset":20865,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3baa3e7fc5768b75e4ef3e64c3cc5297fa42ef19b03bc918ef5352e437e1c168/part-2.html#text"},{"title":"4 Experiments","offset":22037,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3baa3e7fc5768b75e4ef3e64c3cc5297fa42ef19b03bc918ef5352e437e1c168/part-2.html#text"},{"title":"4.1 Experiment Settings","offset":22055,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3baa3e7fc5768b75e4ef3e64c3cc5297fa42ef19b03bc918ef5352e437e1c168/part-2.html#text"},{"title":"4.2 Experiment Results","offset":23669,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3baa3e7fc5768b75e4ef3e64c3cc5297fa42ef19b03bc918ef5352e437e1c168/part-2.html#text"},{"title":"5 Conclusion","offset":31818,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3baa3e7fc5768b75e4ef3e64c3cc5297fa42ef19b03bc918ef5352e437e1c168/part-3.html#text"}],"text_code_points":33142,"source_markdown":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3baa3e7fc5768b75e4ef3e64c3cc5297fa42ef19b03bc918ef5352e437e1c168/source.md"}