OpenAI Blog
Apr 5, 2018
Retro Contest
We’re launching a transfer learning contest that measures a reinforcement learning algorithm’s ability to generalize from previous experience.
Retro Contest: Results
The first run of our Retro Contest—exploring the development of algorithms that can generalize from previous experience—is now complete.
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arXiv AI
Jul 14
GrandCode: Achieving Grandmaster Level in Competitive Programming via Agentic Reinforcement Learning
arXiv:2604. 02721v2 Announce Type: replace Abstract: Competitive programming remains one of the last few human strongholds in coding against AI.
arXiv Machine Learning
Jul 21
From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors
arXiv:2603. 02792v2 Announce Type: replace Abstract: Large Language Models (LLMs) have already been widely adopted for automated algorithm design, demonstrating strong abilities in generating and evolving algorithms across various fields.
arXiv AI
Jun 17
First Proof Second Batch
arXiv:2606. 18119v1 Announce Type: new Abstract: To assess the ability of current AI systems to correctly solve research-level mathematics problems, we tested several AI systems on a set of ten problems in a broad range of mathematical fields; these problems arose naturally in the research process of the contributors.
arXiv AI
Jun 29
SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms
arXiv:2603. 04873v3 Announce Type: replace Abstract: Accurate time series forecasting underpins decision-making in many domains, yetconventional ML development often faces data scarcity, distribution shift, anddiminishing returns from manual iteration.
OpenAI Blog
Nov 24, 2025
GPT-5 and the future of mathematical discovery
UCLA Professor Ernest Ryu and GPT-5 solved a key question in optimization theory, showcasing AI’s role in accelerating mathematical discovery.
arXiv AI
Jul 2
AlgoBench: Benchmarking Algorithmic Adaptation in Code Generation
arXiv:2607. 00062v1 Announce Type: cross Abstract: High pass rates on established programming benchmarks such as HumanEval and LiveCodeBench do not always show whether a model can reason about algorithms.
arXiv Machine Learning
Jun 15
The Program Is Still There: A Conservation Law for Program Discovery
arXiv:2606. 13799v1 Announce Type: cross Abstract: Finding the shortest program that generates a sequence is uncomputable, and for six decades that fact has been mistaken for a wall around finding any generating program.
arXiv Machine Learning
Jun 30
Learning Deterministic Finite-State Machines from the Prefixes of a Single String is NP-Complete
arXiv:2601. 12621v2 Announce Type: replace-cross Abstract: It is well known that computing a minimum deterministic finite automaton consistent with a given set of positive and negative examples is NP-hard.
arXiv Machine Learning
Jul 21
A Survey of Features Used for Representing Black-box Single-objective Continuous Optimization
arXiv:2406. 06629v2 Announce Type: replace Abstract: This survey examines key advancements in designing features to represent optimization problem instances, algorithm instances, and their interactions within the context of single-objective continuous black-box optimization.
arXiv AI
Jun 9
Advancing Mathematics Research with AI-Driven Formal Proof Search
arXiv:2605. 22763v2 Announce Type: replace Abstract: Large language models (LLMs) increasingly excel at mathematical reasoning, but their unreliability limits their utility in mathematics research.
OpenAI Blog
Feb 20
Our First Proof submissions
We share our AI model’s proof attempts for the First Proof math challenge, testing research-grade reasoning on expert-level problems.