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LLMs can outperform random selection in materials optimization, but their effectiveness varies widely across tasks and contexts.
Naive RL fine-tuning for code generation can lead to LLMs regurgitating the same solutions, but penalizing code similarity boosts performance even more than directly optimizing for pass@k.
