Compression Beyond the Uncompressed: A Two-Stage Training Recipe for Soft Context Compression in RAG
Retrieval-Augmented Generation (RAG) improves knowledge-intensive generation by conditioning language models on retrieved documents, but processing these documents becomes increasingly expensive as retrieval depth grows. Soft context compression reduces this cost by encoding documents into compact continuous representations that can be precomputed and reused across queries. However, many existing methods train compressed models by distilling from a full-context teacher. When the teacher is wrong, such distillation can reinforce its errors, while teacher imitation provides no direct signal for improving beyond the teacher. We propose DEX-Comp, a two-stage training recipe that separates rel...
| Authors | Shuyu Guo, Shuo Zhang, Zhaochun Ren |
|---|---|
| Primary category | cs.CL |
| Published | 4 September 2026 |
| Last updated at source | 28 September 2026 |
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