Perplexity Publishes Research on Post-Training Models for Search-Accurate Answers

Perplexity released new research detailing its SFT and RL techniques for making models produce more accurate, search-augmented responses.

Perplexity published research on Wednesday detailing how it post-trains models for search-augmented accuracy, as @perplexity_ai announced. The paper describes a combination of supervised fine-tuning (SFT) and reinforcement learning (RL) aimed at reducing hallucinations when models synthesize answers from retrieved sources.

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