Sakana AI's ShinkaEvolve Revolutionizes LLM Problem-Solving
Sakana AI has unveiled ShinkaEvolve, a revolutionary open-source framework that harnesses large language models (LLMs) in an evolutionary loop to tackle scientific and engineering problems with remarkable efficiency. The project, released under the Apache-2.0 license, has already achieved impressive results, including surpassing AlphaEvolve's circle-packing record with orders-of-magnitude fewer samples.
ShinkaEvolve stands out with its three interconnected components: adaptive parent sampling, novelty-based rejection filtering, and bandit-based LLM ensembling. These work together to evolve programs efficiently, as demonstrated on four diverse domains: circle packing, AIME math reasoning, competitive programming, and LLM training (Mixture-of-Experts).
In the realm of circle packing, ShinkaEvolve has set a new state-of-the-art (SOTA) configuration on the benchmark (n=26) using a mere 150 program evaluations, a significant reduction from the thousands typically required by prior systems. The project maintains an archive of evaluated programs and periodically produces a meta-scratchpad to summarize recent successful strategies. Despite its wide-ranging applications, specific optimization areas remain unclear due to limited search results.
ShinkaEvolve, released with a research report and public code, is available via a GitHub repository featuring a WebUI and examples. Its concrete results include breakthroughs in circle packing, AIME scaffolds, ALE-Bench solutions, and a new MoE load-balancing loss for LLM training. With its open-source nature and impressive performance, ShinkaEvolve promises to accelerate innovation in various scientific and engineering fields.
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