Best-of-Better-$N$: Generating Pre-Aligned Responses with In-Context Learning

• URL: http://arxiv.org/abs/2607.03453v1
• Date: Fri, 03 Jul 2026 16:10:03 GMT
• Title: Best-of-Better-$N$: Generating Pre-Aligned Responses with In-Context Learning
• Abstract summary: Best-of-Better-$N$ (BoBN) is an in context learning-based generation framework to address this challenge.<n>We analytically characterize how in-context learning shifts the output distribution of pretrained transformers toward the high-reward region.
• Score: 20.579613395916024
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Inference-time alignment methods, such as Best-of-$N$, offer a flexible alternative to training-based alignment by using reward models to select high-quality responses generated by a reference LLM. However, the efficacy of these methods is inherently limited by the response quality: if the reference LLM assigns negligible probability to high-reward responses, no selection strategy will succeed in finding aligned outputs. In this work, we propose Best-of-Better-$N$ (BoBN), an in context learning-based generation framework to address this challenge. Our method utilizes retrieval from high-reward examples relevant to the input query and task. Crucially, we introduce a restyling step where retrieved responses are rewritten by the reference LLM to align with the target task's format and style. These restyled examples are used in-context to shift the sampling distribution toward the high-reward region. We analytically characterize how in-context learning shifts the output distribution of pretrained transformers toward the high-reward region, resulting in provable benefits on the target task. We then evaluate BoBN on safety alignment and mathematical reasoning benchmarks across several reference LLMs. BoBN's higher-quality responses enable better performance to be achieved when the number of responses $N$ is fixed, and smaller $N$ required to achieve a target performance.

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