imec AI.labs benchmarks LLM prompt optimization using chess to measure real gains
LudovicDenoyer · x · 2026-10-06
- Researchers at imec AI.labs (Paris) released Benchmarking Prompt Optimization of Large Language Models With Chess.
- They argue automatic prompt optimization (APO) is one of the most effective levers for boosting LLM and agent performance, yet measuring its real gains is surprisingly tricky.
- The work uses chess as an evaluation environment to benchmark prompt optimization, aiming to help develop better optimization algorithms and improve model reasoning.
- Authors include Tristan Karch, Tom Veniat, Karl Tuyls, and Ludovic Denoyer; shared as a thread.
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