Explore Broadly, Reason Sharply: Push Small Models toward the Frontier via Sampling
Parallel Power Tempering improves inference-time power sampling so small models match frontier reasoning without RL updates.
Researchers introduce Parallel Power Tempering (PPT), an inference-time sampling method that boosts LLM reasoning without reinforcement-learning updates or external rewards. Multiple interacting replicas run at different sharpening levels so weaker chains explore diverse trajectories while stronger chains exploit high-likelihood answers. The method addresses truncation bias in earlier power samplers and studies swap strategies under finite memory and compute. Experiments show better reasoning traces than single-chain power sampling and performance competitive with RL-post-trained and frontier models.
- PPT runs parallel replicas at different power-sharpening levels.
- Lower-power chains explore; higher-power chains exploit likely answers.
- Method mitigates truncation bias in prior power samplers.
- Authors report gains over single-chain sampling and RL post-training.
From the original article
1 min read at huggingface.co
Power-sharpened sampling is an inference-time alternative to reinforcement-learning (RL) post-training for enhancing reasoning in large language models (LLMs). High-probability sequences are amplified under the base model without parameter updates or external rewards, avoiding the costly optimization and jagged generalization of RL.
By Panagiotis Theodoropoulos, Nan Jiang, Xintong Duan · huggingface.co
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