CLaRa: Bridging Retrieval and Generation with Continuous Latent Reasoning
Retrieval-augmented generation (RAG) enhances large language models (LLMs) with external knowledge but still suffers from long contexts and disjoint retrieval-generation optimization. In this work, we propose CLaRa (Continuous Latent Reasoning), a unified framework that performs embedding-based compression and joint optimization in a shared continuous space. To obtain semantically rich and retrievable compressed vectors, we introduce SCP, a key-preserving data synthesis framework using QA and paraphrase supervision. CLaRa then trains the reranker and generator end-to-end via a single language modeling loss, with gradients flowing through both modules using a differentiable top-k estimator. Theoretically, this unified optimization aligns retrieval relevance with answer quality. Experiments across multiple QA benchmarks show that CLaRa achieves state-of-the-art compression and reranking performance, often surpassing text-based fine-tuned baselines.
69260a8b45972f0c0eea1713
Jie He
69260a8b45972f0c0eea1714
Richard He Bai
claimed_verified
2025-12-22T11:01:25.591Z
5f024bd8ff69163f6f59e775
https://cdn-avatars.huggingface.co/v1/production/uploads/1652111948107-5f024bd8ff69163f6f59e775.png
Richard He Bai
richardbaihe
user
richardbaihe
69260a8b45972f0c0eea1715
Sinead Williamson
69260a8b45972f0c0eea1716
Jeff Z. Pan
69260a8b45972f0c0eea1717
Navdeep Jaitly
69260a8b45972f0c0eea1718
Yizhe Zhang
CLaRa: Bridging Retrieval and Generation with Continuous Latent Reasoning
Retrieval-augmented generation (RAG) enhances large language models (LLMs) with external knowledge but still suffers from long contexts and disjoint retrieval-generation optimization. In this work, we propose CLaRa (Continuous Latent Reasoning), a unified framework that performs embedding-based compression and joint optimization in a shared continuous space. To obtain semantically rich and retrievable compressed vectors, we introduce SCP, a key-preserving data synthesis framework using QA and paraphrase supervision. CLaRa then trains the reranker and generator end-to-end via a single language modeling loss, with gradients flowing through both modules using a differentiable top-k estimator. Theoretically, this unified optimization aligns retrieval relevance with answer quality. Experiments across multiple QA benchmarks show that CLaRa achieves state-of-the-art compression and reranking performance, often surpassing text-based fine-tuned baselines.
CLaRa enhances retrieval-augmented generation by introducing unified embedding-based compression and joint optimization, achieving state-of-the-art performance in QA benchmarks.
RAG large language models external knowledge long contexts disjoint retrieval-generation optimization CLaRa continuous latent reasoning embedding-based compression joint optimization shared continuous space SCP key-preserving data synthesis QA paraphrase supervision reranker generator differentiable top-k estimator