Inference-Time Alignment

Greedy, Not Needy - A General Paradigm for Efficient Decoding in Large Language Models
Adaptively focuses computation on the most critical early tokens during LLM decoding, boosting alignment performance across multiple tasks compared to Best-of-N and fine-tuning.
Greedy, Not Needy - A General Paradigm for Efficient Decoding in Large Language Models
STARS - Segment-level Token Alignment via Rejection Sampling in Large Language Models
Decoding method that aligns large language models with human preferences at inference time by accepting only high-reward text segments, boosting quality without retraining.
STARS - Segment-level Token Alignment via Rejection Sampling in Large Language Models