arXiv:2607.03453v1 Announce Type: cross 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 challen
The continuous improvement in large language models requires increasingly sophisticated alignment techniques, and the limitations of current inference-time methods like Best-of-N are becoming apparent.
Improving the ability of LLMs to generate high-quality, aligned responses directly impacts the reliability and utility of AI systems, accelerating their deployment and integration into workflows.
This method potentially offers a more effective way to align LLM outputs without extensive retraining, leading to more robust and usable AI agents and applications.
- · AI developers
- · Companies deploying LLMs
- · Researchers in AI alignment
- · Companies relying solely on basic Best-of-N methods
Pre-aligned responses reduce the need for extensive post-generation filtering and human oversight of LLM outputs.
More reliable LLMs accelerate the development and adoption of AI agents that can autonomously perform complex tasks.
Increased trustworthiness of AI outputs could lead to broader societal acceptance and integration of autonomous AI systems.
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Read at arXiv cs.AI