🌐 Maintaining diversity: RL fine-tuning risks narrowing output diversity, however, output diversity is crucial for evolutionary search. We incentivize diversity through forward KL regularization, an island-based program database, and more. 9/12

📊 Initially, the distributions of program scores of EvoTune and the baseline are similar. However, as the search progresses, EvoTune generates a larger number of high-quality solutions as reflected by the larger increase in the lower optimality gap region. 8/12

We evaluate EvoTune on three combinatorial optimization problems:
📦 Bin packing
🗺️ Traveling salesman
🧩 Flatpack

📈 Key result: Across all problems and three LLMs, EvoTune discovers programs with higher rewards faster compared to the Funsearch (no finetune) baseline 7/12

Excited to share our latest work on EvoTune, a novel method integrating LLM-guided evolutionary search and reinforcement learning to accelerate the discovery of algorithms! 1/12🧵