Scoopfeeds — Intelligent news, curated.
agentic-ai

Practical Learnings from Synthetic Document Finetuning

LessWrong · May 26, 2026, 7:22 PM

We've been using Synthetic Document Finetuning (SDF) quite a bit at Apollo Research lately. This post covers a few tweaks to the standard SDF recipe specific to our use cases, plus some general tips and tricks for getting good results. We’re sharing these notes in case they’re useful to others doing research with SDF.1. What Is SDF?Synthetic Document Finetuning (SDF) is a knowledge editing technique where models are finetuned on LLM-generated documents consistent with a target fact or belief. As described in Slocum et al. (2025), SDF "often succeeds at implanting beliefs that behave similarly to genuine knowledge." These implanted beliefs can generalize to related contexts, are often robust to scrutiny, and form internal representations similar to genuine knowledge.We mostly followed the pipeline described in Slocum et al. (2025) and the safety-research/false-facts repository.The pipeline has several stages:Universe Context: Define a "universe" description where the target belief is true.Fact Extraction: Extract discrete claims from that universe that the synthetic documents will revolve around.Generation: Use an LLM to generate a large, diverse corpus of synthetic documents. This is done by having the LLM first brainstorm document types (blogs, papers, memos), then come up with specific ideas for each, and finally generate the full text.Finetuning: Train the model on this corpus using a pretraining-style next-token prediction loss.We mostly used Claude Sonnet 4.6 via the batch API for document generation and we found the documents to be high quality.Iterating on Universes and Generation PromptsWhen setting this up, we suggest starting small: generate about 5 documents, read through them to find things that are wrong or not quite right, update the universe description and prompt, and iterate until the results are good quality and you're getting what you want.Doing a round of model-graded quality filtering on the final generated dataset can also prove useful, especia

Article preview — originally published by LessWrong. Full story at the source.
Read full story on LessWrong → More top stories
Aggregated and edited by the Scoop newsroom. We surface news from LessWrong alongside other reporting so you can compare coverage in one place. Editorial policy · Corrections · About Scoop