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Particle filtering reveals a fundamental limit to inference-time sampling methods for LLMs, suggesting that simply increasing the number of samples has diminishing returns.
Sampling from diffusion models with quadratic rewards can be surprisingly hard: negative-definite tilts are intractable even in simple cases, while a new algorithm makes low-rank positive-definite tilts tractable.