Particle-filtering selection of LLM reasoning paths scales inference-time compute 4 to 16 times more efficiently than beam search on math tasks and lets small models match much larger closed models.
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Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods
Particle-filtering selection of LLM reasoning paths scales inference-time compute 4 to 16 times more efficiently than beam search on math tasks and lets small models match much larger closed models.