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Discovering algorithms with computational language processing
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AI
DOI：10.1126/sciadv.aea4216.png)
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Abstract
En 中文
We present a framework automating algorithm discovery by bootstrapping their natural conceptualization as sequences of operations, represented as tokens. These computational tokens are chained using a grammar, enabling the formation of increasingly sophisticated procedures. Our ensemble Monte Carlo tree search guided by reinforcement learning explores token chaining and drives the creation of new tokens via byte-pair encoding. This methodology rediscovers, improves, and generates new algorithms that substantially outperform existing methods for strongly nondeterministic polynomial-time–hard combinatorial optimization problems and foundational quantum computing approaches such as Grover’s and the quantum approximate optimization algorithm. Operating at the computational rather than code-generation level, our framework produces algorithms that can be tailored specifically to problem instances, not merely classes.
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