GRC: Unifying Reasoning-Driven Generation, Retrieval and Compression
Text embedding and generative tasks are usually trained separately based on large language models (LLMs) nowadays. This causes a large amount of training cost and deployment effort. Context compression is also a challenging and pressing task, which is vital to reasoning-driven generation, and agentic tasks requiring long context and continual learning. In this paper, we explore how to unify reasoning-driven generation, reasoning-enhanced text representation and context compression tasks in one forward pass for LLMs. Through meta latent tokens and a unified generative, representative and compressive tuning approach, we propose a training framework named GRC that bridges the three tasks. Th...
| Authors | Zhongtao Miao, Qiyu Wu, Yoshimasa Tsuruoka |
|---|---|
| Primary category | cs.CL |
| Published | 9 May 2026 |
| Last updated at source | 12 May 2026 |
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