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Algorithm Generation

Teaching AI to devise and implement algorithms

微软研究院博客

A flow chart demonstrating the five steps in a self-play pipeline for a language model to improve itself automatically.A self-play pipeline for a language model (LM) to improve itself in a fully automatic manner. First, the LM generates novel puzzles based on a training set of handwritten puzzles. Then, the LM attempts to solve each of these puzzles 100 times. In Step 3, the computer (specifically a Python interpreter) filters the candidate solutions for correctness. Finally, the LM is improved by further training on these verified correct solutions to synthetic puzzles, and the process repeats. This process leads to significant improvements as measured on held-out test puzzles that were also handwritten.
微软研究院博客

| Adam Tauman Kalai 和 Patrick Haluptzok

Self-play has helped AI systems succeed in games like chess and Go. Can the same method help improve AI programming abilities? Using easy-to-check, hard-to-solve programming problems, researchers show AI can create, solve, and train on its own puzzles.