Explore Broadly, Reason Sharply: Push Small Models toward the Frontier via Sampling
Parallel Power Tempering improves inference-time reasoning, matching frontier models without RL post-training.
Parallel Power Tempering is an inference-time sampling method that replaces RL post-training for LLM reasoning. Multiple interacting replicas run at different power-sharpening levels so weaker chains explore and stronger chains exploit higher-likelihood answers, while a fix addresses truncation bias in prior power samplers. Experiments show better traces than single-chain power sampling and RL-post-trained models, with performance comparable to frontier models and no weight updates or external rewards.
- Parallel Power Tempering runs interacting replicas at different sharpening levels.
- Lower-power replicas explore while higher-power chains exploit sharper targets.
- The sampler mitigates truncation bias identified in earlier power samplers.
- It beats single-chain power sampling and RL-post-trained models.
- No parameter updates or external rewards are required at inference time.
From the original article
1 min read at arxiv.orgPower-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 · arxiv.org
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