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🧠 AI🟢 BullishImportance 7/10
Thompson Sampling via Fine-Tuning of LLMs
arXiv – CS AI|Nicolas Menet, Aleksandar Terzi\'c, Michael Hersche, Andreas Krause, Abbas Rahimi||5 views
🤖AI Summary
Researchers developed ToSFiT (Thompson Sampling via Fine-Tuning), a new Bayesian optimization method that uses fine-tuned large language models to improve search efficiency in complex discrete spaces. The approach eliminates computational bottlenecks by directly parameterizing reward probabilities and demonstrates superior performance across diverse applications including protein search and quantum circuit design.
Key Takeaways
- →ToSFiT eliminates the need for expensive acquisition function maximization in Bayesian optimization by leveraging pre-trained LLM knowledge.
- →The method provides theoretical regret bounds matching standard Thompson sampling while offering better computational efficiency.
- →Empirical validation shows state-of-the-art sample efficiency across FAQ refinement, protein search, and quantum circuit design tasks.
- →The approach outperforms existing methods including in-context Bayesian optimization, reinforcement learning, and evolutionary search.
- →The research demonstrates practical applications of LLMs in scientific optimization problems beyond traditional language tasks.
#machine-learning#bayesian-optimization#llm#fine-tuning#thompson-sampling#optimization#research#arxiv
Read Original →via arXiv – CS AI
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