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Inject, Align, Recover: Staged Post-Training for Retrieval-Free Document Knowledge Internalization

IAR turns a fixed document corpus into parametric knowledge through separate injection, QA alignment, and recovery stages. It improved domain QA while retaining more general capability than vanilla SFT.

arXiv

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Source Summary

IAR separates retrieval-free document internalization into **Inject, Align, and Recover**. Documents become continuation, rewrite, and reconstruction objectives; answer-only QA follows; then the adapted model is merged with the base instruction model.

Practical Implication

For bounded corpora that must work without runtime retrieval, separate knowledge ingestion from answer behavior and general-capability recovery. The method improved all four reported metrics over Vanilla SFT in **7 of 8 settings**.

Agent-Ready Context

IAR separates retrieval-free document internalization into **Inject, Align, and Recover**. Documents become continuation, rewrite, and reconstruction objectives; answer-only QA follows; then the adapted model is merged with the base instruction model.

For bounded corpora that must work without runtime retrieval, separate knowledge ingestion from answer behavior and general-capability recovery. The method improved all four reported metrics over Vanilla SFT in **7 of 8 settings**.

Average gains were **3.6 percentage points** in domain QA and **12.1 points** in mean general performance. LoRA and FAPM still won some individual general metrics, so IAR is a frontier tradeoff rather than a universal winner.

Context Map

modelresearchdata#retrieval#context-engineering

Uncertainty

Average gains were **3.6 percentage points** in domain QA and **12.1 points** in mean general performance. LoRA and FAPM still won some individual general metrics, so IAR is a frontier tradeoff rather than a universal winner.